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Face detection dataset for deep learning

The github repo with final model and a subset of FDDB dataset for training can be found at https://github.com/quanhua92/darknet Face Detection With Deep Learning. There are myriad of methods demonstrated for face detection and out of all methods, the Multi-Task Cascaded Convolutional Neural Network or MTCNN for short, described by Kaipeng Zhang, et al. in the 2016 paper titled Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks. The network uses a cascade structure with three. MNIST is one of the most popular deep learning datasets out there. It's a dataset of handwritten digits and contains a training set of 60,000 examples and a test set of 10,000 examples. It's a good database for trying learning techniques and deep recognition patterns on real-world data while spending minimum time and effort in data.

In this tutorial, you learned how to create a COVID-19 face mask detector using OpenCV, Keras/TensorFlow, and Deep Learning. To create our face mask detector, we trained a two-class model of people wearing masks and people not wearing masks. We fine-tuned MobileNetV2 on our mask/no mask dataset and obtained a classifier that is ~99% accurate The dataset also includes helpful metadata in CSV format. 8. Real and Fake Face Detection. This dataset was made to train facial recognition models to distinguish real face images from generated face images. The dataset includes over 1,000 real face images and over 900 fake face images which vary from easy, mid, and hard recognition difficulty. 9 Hi, It really depends on your project and if you want images with faces already annotated or not. Here are a few of the best datasets from a recent compilation I made: UMDFaces - this dataset includes videos which total over 3,700,000 frames of an..

Deep Learning Face Detection with Darknet YOLO - YouTub

  1. Face detection is a computer vision problem that involves finding faces in photos. It is a trivial problem for humans to solve and has been solved reasonably well by classical feature-based techniques, such as the cascade classifier. More recently deep learning methods have achieved state-of-the-art results on standard benchmark face detection datasets
  2. UMD Faces Annotated dataset of 367,920 faces of 8,501 subjects. CASIA WebFace Facial dataset of 453,453 images over 10,575 identities after face detection. Requires some filtering for quality. MS-Celeb-1M 1 million images of celebrities from around the world. Requires some filtering for best results on deep networks
  3. Gender & Age Classification using OpenCV Deep Learning ( C++/Python ) Vikas Gupta. February 19, 2019 Leave a Comment. February 19, 2019 By Leave a Comment. In this tutorial, we will discuss an interesting application of Deep Learning applied to faces. We will estimate the age and figure out the gender of the person from a single image. The model is trained by Gil Levi and Tal Hassner. We will.
  4. Gender detection (from scratch) using deep learning with keras and cvlib. The keras model is created by training SmallerVGGNet from scratch on around 2200 face images (~1100 for each class). Face region is cropped by applying face detection using cvlib on the images gathered from Google Images. It acheived around 96% training accuracy and ~90% validation accuracy
  5. STL-10 dataset is an image recognition dataset for developing unsupervised feature learning, deep learning, self-taught learning algorithms. It is inspired by the CIFAR-10 dataset but with some modifications
  6. Size: The size of the dataset is 200K, which includes 10,177 number of identities, 202,599 number of face images, and 5 landmark locations, 40 binary attributes annotations per image. Projects: The dataset can be employed as training and testing sets for the following computer vision tasks: face attribute recognition, face detection, landmark (or facial part) localisation, and face editing.
  7. Facial Expression Detection with Deep Learning (Keras) V.2 Object Detection on Custom Dataset with TensorFlow 2 and Keras in Python - Duration: 44:00. Venelin Valkov 11,067 views. 44:00. YOLO.

Few weeks before, I thought to explore face recognition using deep learning based models. This blog-post demonstrates building a face recognition system from scratch. Introduction. A face recognition system comprises of two step process i.e. face detection (bounded face) in image followed by face identification (person identification) on the detected bounded face. The following two techniques. In this tutorial, we will discuss the various Face Detection methods in OpenCV and Dlib and compare the methods quantitatively. We will share code in C++ and Python for the following Face Detectors : Haar Cascade Face Detector in OpenCV Deep Learning based Face Detector in OpenCV HoG Face Detector in Dlib Deep Learning based [

Face Detection with Deep Learning using Multi Tasked

These datasets are used for machine-learning research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets Creating Multi-View Face Recognition/Detection Database for Deep Learning in Programmatic Way . Ahmet ÖZLÜ. Follow. Sep 30, 2017 · 6 min read. Let's assume that you want to investigate some. Caffe-face - Caffe Face is developed for face recognition using deep neural networks. Norm-Face - Norm Face, finetuned from center-face and Light-CNN. Tutorial. Deep Learning for Face Recognition. Shiguan Shan, Xiaogang Wang, and Ming yang. Software. OpenCV. With some trained face detector models. dlib. Dlib implements a state-of-the-art of. Swapped Face Detection using Deep Learning and Subjective Assessment Xinyi Ding , Zohreh Raziei y, Eric C. Larson , Eli V. Olinick , Paul Krueger z, Michael Hahslery Department of Computer Science, Southern Methodist University xding@mail.smu.edu, eclarson@lyle.smu.edu yDepartment of Engineering Management, Information and Systems, Southern Methodist University zraziei@mail.smu.edu, folinick.

25 Open Datasets for Deep Learning Every Data Scientist

  1. 20 Free Image Datasets for Computer Vision. Article by Meiryum Ali | May 22, 2019. What is computer vision? Computer vision enables computers to understand the content of images and videos. The goal in computer vision is to automate tasks that the human visual system can do. Computer vision tasks include image acquisition, image processing, and image analysis. The image data can come in.
  2. read. Facial recognition is a thriving application of deep learning. From phones to airport.
  3. In order to effectively prevent the spread of COVID-19 virus, almost everyone wears a mask during coronavirus epidemic. This almost makes conventional facial recognition technology ineffective in many cases, such as community access control, face access control, facial attendance, facial security checks at train stations, etc..
  4. Detection of Face Morphing Attacks by Deep Learning Clemens Seibold 1, Wojciech Samek , Anna Hilsmann and Peter Eisert1;2 1 Fraunhofer HHI, Einsteinufer 37, 10587 Berlin, Germany 2 Humboldt University Berlin, Unter den Linden 6, 10099 Berlin, Germany Abstract. Identi cation by biometric features has become more popular in the last decade
  5. deep learning approach using neural network has achieved significant success in tackling face detection as a subclass of object classification, localization, and detection. Appar-ently, the evolve of face detection correlates closely with the development of object classification, localization and detec-tion techniques. 2.1. Sliding Windo
  6. An On-device Deep Neural Network for Face Detection Vol. 1, Issue 7 ∙ Apple started using deep learning for face detection in iOS 10. With the release of the Vision framework, developers can now use this technology and many other computer vision algorithms in their apps. We faced significant challenges in developing the framework so that we could preserve user privacy and run efficiently.

COVID-19: Face Mask Detector with OpenCV, Keras/TensorFlow

5 Million Faces — Top 15 Free Image Datasets for Facial

What are the best datasets for face recognition? - Quor

detection technology by using deep learning. The main idea used in this project is multi-task Cascaded Convolu-tional Neural Networks, which contains three sub-networks together to learn to recognize human faces after several stages of decomposition and filtering. The dataset that is going to be used is FDDB dataset, which contains over five thousand faces in a set of around two thousand and. Learn how to develop a face recognition system by leveraging deep learning. Find out how to code for face detection, identification, and more The first (of many more) face detection datasets of human faces especially created for face detection (finding) instead of recognition: BioID Face Detection Database 1521 images with human faces, recorded under natural conditions, i.e. varying illumination and complex background. The eye positions have been set manually (and are included in the set) for calculating the accuracy of a face.

How to Perform Face Detection with Deep Learning

  1. With OpenCV you can perform face detection using pre-trained deep learning face detector model which is shipped with the library. When OpenCV 3.3 was officially released, it has highly improved deep neural networks (dnn) module. The primary contributor to this module was Aleksandr Rybnikov, and Rybnikov included accurate, deep learning face detector. Caffe-based face detector can be found in.
  2. To do this, we have utilised both controlled and uncontrolled public facial datasets through which we show how deep learning can be utilised for face recognition using imperfect facial cues. Thus, given some partial facial data, we show how feature extraction can be performed using popular CNNs such as the VGGF model. We show how classifiers based on popular SVMs as well as CS can be utilised.
  3. In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels. If the real age estimation research spans over decades, the study of apparent age estimation or the age as perceived by other humans from a face image is a.
  4. g Ge1, Datasets for Face Detection In the literature, many datasets have been constructed to assess face detection models. While early datasets mainly consist of images collected in the controlled environment, recent datasets like MALF [30], IJB-A [13],CelebA [32] and WIDER FACE [31] tend to collect images from In-ternet. In this.

Open Datasets Pathmin

There are different kinds of methods used for Face Recognition, but the best are based on Deep Learning algorithms. They are commonly used these days. The deep learning algorithms project a face. Facial emotion detection using deep learning Daniel Llatas Spiers The use of machines to perform different tasks is constantly increasing in society. Providing machines with perception can lead them to perform a great variety of tasks; even very complex ones such as elderly care. Machine perception requires that machines understand about their environment and interlocutorÕs intention.

Image Segmentation in Deep Learning: Methods and Applications Modern Computer Vision technology, based on AI and deep learning methods, has evolved dramatically in the past decade. Today it is used for applications like image classification, face recognition, identifying objects in images, video analysis and classification, and image processing in robots and autonomous vehicles This model is composed of several essential steps developed using today's most advanced techniques: CNN cascade for face detection and CNN for generating face embeddings. The primary goal of this research was the practical employment of these state-of-the-art deep learning approaches for face recognition tasks. Due to the fact that CNNs achieve the best results for larger datasets, which is.

Gender & Age Classification using OpenCV Deep Learning

Face Detection and Recognition Using OpenCV: Python Hog Tutorial This post may contain affiliate links. Please read disclosurefor more info. Reading Time: 4 minutes. Face Detection is currently a trending technology. You look out the offline world and internet world everywhere you see faces. Faces in pictures as well as in Videos. Of course, Our brain easily identifies the person in the. Disguised face recognition is still quite a challenging task for neural networks and primarily due to the lack of corresponding datasets. In this article, we are going to feature several face datasets presented recently. Each of them reflects different aspects of face obfuscation, but their goal is the same - to help developers create better models for disguised face recognition

Face Detection => 本論文の範囲外(Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans) 2. Face Alignment => 本論文の範囲外(Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans) 3. Deep Face Recognition • FRのタスクは,大きく以下の2つに分類 1. Face Verification: 1対1の類似度判定. ConvNet Configuration [11] 6 Amit Dhomne et al. / Procedia Computer Science 00 (2018) 000â€000 Deep CNN(D-CNN) is using in this area also including Articulated pose estimation, Body configuration parsing, face parsing, Face recognition, object detection, path detection, plant disease estimation through the image of plant leaves, age and expression recognition through the face of human. Keywords: Face detection, deep learning, deep model, part-based, detection rate, false positive rate, recall rate 1. Introduction Face detection is a computer technology that determines the locations and sizes of human faces in digital images, which is a key technology in face information processing. It has been widely applied to pattern recognition, identity authentication, human computer.

Building a Facial Recognition Pipeline with Deep Learning in Tensorflow. Originally published by Cole Murray on July 2nd 2017 @ColeMurrayCole Murray. In my last tutorial , you learned about convolutional neural networks and the theory behind them. In this tutorial, you'll learn how to use a convolutional neural network to perform facial recognition using Tensorflow, Dlib, and Docker. This dataset is an image classification dataset to classify room images as bedroom, kitchen, bathroom, living room, exterior, etc. Images from different houses are collected and kept together as a dataset for computer testing and training. This dataset helps for finding which image belongs to which part of house Then, we will have a look at the first program of an HDevelop example series on object detection. Within this program, we will have a look how to read in a dataset that you labeled, for example, with the MVTec Deep Learning Tool. Afterwards we will split this dataset and preprocess the labeled data to be suitable for the deep learning model To build a gender and age detector that can approximately guess the gender and age of the person (face) in a picture using Deep Learning on the Adience dataset. Gender and Age Detection - About the Project. In this Python Project, we will use Deep Learning to accurately identify the gender and age of a person from a single image of a face The BioID Face Database has been recorded and is published to give all researchers working in the area of face detection the possibility to compare the quality of their face detection algorithms with others. It may be used for such purposes without further permission. During the recording special emphasis has been placed on real world conditions. Therefore the testset features a large.

Viola-Jones face detector 5:41. Attentional cascades and neural networks 3:21. Taught By. Anton Konushin. Senior Lecturer. Alexey Artemov. Senior Lecturer . Try the Course for Free. Transcript. In this video, I will describe a seminal Viola-Jones face detection algorithm. I believe it is useful to understand its key ideas even in our deep learning era. In object detection with sliding windows. Deep Learning for Emotion Recognition on Small Datasets Using Transfer Learning Hong-Wei Ng, Viet Dung Nguyen, Vassilios Vonikakis, Stefan Winkler Advanced Digital Sciences Center (ADSC) University of Illinois at Urbana-Champaign, Singapore {hongwei.ng, vietdung.n, bbonik, stefan.winkler}@adsc.com.sg ABSTRACT This paper presents the techniques employed in our team's submis-sions to the 2015.

Gender detection (from scratch) using deep learning with

  1. imize the noise that exists outside the person's face in an image. We made use of th
  2. Dataset for benchmarking anomaly detection algorithms. It contains images from 15 different object and texture categories. Each category consists of defect-free training images, as well as test images that contain various types of defects. Furthermore, pixel-precise ground truth annotations of the defects are provided
  3. About Jon Barker Jon Barker is a Senior Research Scientist in the Applied Deep Learning Research team at NVIDIA. Jon joined NVIDIA in 2015 and has worked on a broad range of applications of deep learning including object detection and segmentation in satellite imagery, optical inspection of manufactured GPUs, malware detection, resumé ranking and audio denoising

This paper presents a baseline for classification performance on the dataset using the benchmark deep learning models, Inception-v3 and ResNet-50. These models achieved an average classification. Thank you for posting this question . Tensorflow's object detection API is an amazing release done by google. Here , they have reduced much of the burden on an developers head , by creating really good scripts for training and testing along with a..

Typically used for object detection (i.e., predicting the (x;y)-coordinates of the bounding box for a particular object in an image), we can use CALTECH-101 to study deep learning algorithms as well. The dataset of 8,677 images includes 101 categories spannin g a diverse range of objects, including elephants, bicycles, soccer balls, and even human brains, just to name a few Z. Xie, P. Jiang, S. Zhang, Fusion of LBP and hog using multiple kernel learning for infrared face recognition, in 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) (May 2017), pp. 81-84 Google Schola Image based Static Facial Expression Recognition with Multiple Deep Network Learning Zhiding Yu Carnegie Mellon University 5000 Forbes Ave Pittsburgh, PA 15213 yzhiding@andrew.cmu.edu Cha Zhang Microsoft Research One Microsoft Way Redmond, WA 98052 chazhang@microsoft.com ABSTRACT We report our image based static facial expression recogni-tion method for the Emotion Recognition in the Wild Chal.

Datasets « Deep Learning

Output Face detection Face detection in live video with OpenCV and deep learning. Create python file name call FaceDetectorVideo.py inside the src/and code following lines # import the necessary packages from imutils.video import VideoStream import numpy as np import argparse import imutils import time import cv Machine Learning Datasets for Deep Learning. 1. Youtube 8M Dataset. The youtube 8M dataset is a large scale labeled video dataset that has 6.1millions of Youtube video ids, 350,000 hours of video, 2.6 billion audio/visual features, 3862 classes and 3avg labels per video. It is used for video classification purposes. 1.1 Data Link: Youtube 8 Face detection applications use algorithms that determine whether images are positive images (i.e. images with a face) or negative images (i.e. images without a face). To be able to do this accurately, the algorithms must be trained on huge datasets containing hundreds of thousands of face images and non-face images FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces ining the authenticity of images. Here, we believe the recent advances in deep learning offer a unique opportunity due to the ability to learn extremely powerful image fea- tures with convolutional neural networks (CNNs). In particular, supervised training has shown to produce extremely impressive results, and.

Video: 10 Face Datasets To Start Facial Recognition Project

Facial Expression Detection with Deep Learning (Keras) V

This example shows how to train an object detector using deep learning and R-CNN (Regions with Convolutional Neural Networks). Overview. This example shows how to train an R-CNN object detector for detecting stop signs. R-CNN is an object detection framework, which uses a convolutional neural network (CNN) to classify image regions within an image [1]. Instead of classifying every region using. This article is a comprehensive review of Data Augmentation techniques for Deep Learning, specific to images. This is Part 2 of How to use Deep Learning when you have Limited Data. Checkout Part 1 here. We have all been there. You have a stellar concept that can be implemented using a machine learning model. Feeling ebullient, you open your web. TensorFlow For JavaScript For Mobile & IoT For Production Swift for TensorFlow (in beta) API r2.2 (stable) r2.1 r2.0 API r1 r1.15 More Models & datasets Tools Libraries & extensions TensorFlow Certificate program Learn ML About Case studies Trusted Partner Progra Face detection. Since the mid-2000s some point and shoot cameras started to come with the feature of detecting faces for a more efficient auto-focus. While it's a narrower type of object detection, the methods used apply to other types of objects as we'll describe later. Counting. One simple but often ignored use of object detection is counting. The ability to count people, cars, flowers. Dataset Files. Channel data for waterbath and phantom experiments. Channel data, annotations, and data splits for training network. Pretained network

Yet Another Face Recognition Demonstration on Images

Detect the location of keypoints on face images Detect the location of keypoints on face images Facial Keypoints Detection Detect the location of keypoints on face images . 175 teams; 3 years ago; Overview Data Notebooks Discussion Leaderboard Rules. Join Competition. Overview. description evaluation submission-instructions getting-started-with-r deep-learning-tutorial. The objective of. deep learning 如何实现face detection呢? 这里介绍yahoo最新的一篇论文Multi-view Face Detection Using Deep Convolutional Neural Netwo. 程序园 . 栏目; 标签; 分类; 教程; 代码; deep learning for face detection. 时间 2015-06-03. 标签 face detection deep learning. 更新: 我把用alex-net finetune的aflw model 放在百度云盘上,欢迎大家下载: http. Deep Learning Experiment. A multi-layered neural network with 3 hidden layers of 125, 25 and 5 neurons respectively, is used to tackle the task of learning to identify emotions from text using a bi-gram as the text feature representation. The settings for this experiment can be found in The Details section

Enhancing Intelligent Video Analytics with Machine Learning Dennis Sng Deputy Director & Principal Scientist NVIDIA AI Conference 24 October 2017 ROSE LAB OVERVIEW 2. 24/10/2017 2 Visual Object Categorisation • 2D (Planar) objects: Logos, book covers, CD covers, labels • 3D rigid objects: Cars, hardware, product packages • Deformable objects: Clothes, shoes, bags, toys • Faces. 3. Supervised learning on the iris dataset¶ Framed as a supervised learning problem. Predict the species of an iris using the measurements; Famous dataset for machine learning because prediction is easy; Learn more about the iris dataset: UCI Machine Learning Repositor Contributing Data to Deepfake Detection Research Tuesday, September 24, 2019 Posted by Nick Dufour, Google Research and Andrew Gully, Jigsaw Deep learning has given rise to technologies that would have been thought impossible only a handful of years ago. Modern generative models are one example of these, capable of synthesizing hyperrealistic images, speech, music, and even video. These models. Deep Learning Face Attributes in the Wild∗ Ziwei Liu1,3 Ping Luo3,1 Xiaogang Wang2,3 Xiaoou Tang1,3 1Department of Information Engineering, The Chinese University of Hong Kong 2Department of Electronic Engineering, The Chinese University of Hong Kong 3Shenzhen Key Lab of Comp. Vis. & Pat. Rec., Shenzhen Institutes of Advanced Technology, CAS, China. Hey! In this detailed guide, I will explain how Deep Learning can be used in the field of Anomaly Detection. Furthermore, I will explain how to implement a Deep Neural Network Model for Anomaly Detection in TensorFlow 2.0. All source code and the corresponding dataset is, of course, available for you to download- nice ;

Video: Face Detection - OpenCV, Dlib and Deep Learning Learn OpenC

Part 1: Artificial Neural Networks (ANN) Datasets & Templates: Artificial-Neural-Networks; Additional Reading: Yann LeCun et al., 1998, Efficient BackProp By Xavier Glorot et al., 2011 Deep sparse rectifier neural networks; CrossValidated, 2015, A list of cost functions used in neural networks, alongside applications Andrew Trask, 2015, A Neural Network in 13 lines of Python (Part 2. Detection and diagnosis tools offer a valuable second opinion to the doctors and assist them in the screening process. This type of mechanism would also assist in providing results to the doctors quickly. In this blog, we are applying a Deep Learning (DL) based technique for detecting COVID-19 on Chest Radiographs using MATLAB Application: A Face Detection Pipeline < In-Depth: Kernel Density Estimation | Contents | Further Machine Learning Resources > This chapter has explored a number of the central concepts and algorithms of machine learning. But moving from these concepts to real-world application can be a challenge. Real-world datasets are noisy and heterogeneous, may have missing features, and data may be in a.

With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy, and optimization function. In this paper, we provide a review of deep learning-based object detection frameworks. First of all, you need to have a proper understanding of neural networks architecture of Faceboxes, here I have explained n detail How any face detection deep learning models work and what are the application of face detection. What is the architecture of Faceboxes and some problems that previous deep learning models were not able to solve and Faceboxes solved it Basic Face Detection. Lets, do something fun such as detecting a face. To detect face we will use an open source xml stump-based 20x20 gentle adaboost frontal face detector originally created by Rainer Lienhart. A good post with details on Haar-cascade detection is here. Face detection using OpenCV Face Detection+recognition: This is a simple example of running face detection and recognition with OpenCV from a camera. NOTE: I MADE THIS PROJECT FOR SENSOR CONTEST AND I USED CAMERA AS A SENSOR TO TRACK AND RECOGNITION FACES.So, Our GoalIn this session, 1. Install Anacond.. 2 PARKHI et al.: DEEP FACE RECOGNITION. Dataset Identities Images LFW 5,749 13,233 WDRef [4] 2,995 99,773 CelebFaces [25] 10,177 202,599 Dataset Identities Images Ours 2,622 2.6M FaceBook [29] 4,030 4.4M Google [17] 8M 200M Table 1: Dataset comparisons: Our dataset has the largest collection of face images outside industrial datasets by Goole, Facebook, or Baidu, which are not publicly.

Several deep learning techniques for object detection exist, including Faster R-CNN and you only look once (YOLO) v2. This example trains a Faster R-CNN vehicle detector using the trainFasterRCNNObjectDetector function. For more information, see Object Detection using Deep Learning Both libraries implement the most recent deep-learning algorithms for object detection. Detectron is available as a Python library available under the Apache 2.0 license and is built on Caffe2 , a. Upgrading your machine learning, AI, and Data Science skills requires practice. To practice, you need to develop models with a large amount of data. Finding good datasets to work with can be challenging, so this article discusses more than 20 great datasets along with machine learning project ideas for yo

We demonstrate classification and detection on this dataset using a neural network we call ResCeption. G., Sakla W.A., Boakye K. (2016) A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision - ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9907. Springer, Cham. Real and Fake Face Detection. Computational Intelligence and Photography Lab Department of Computer Science, Yonsei University . Fake Face Photos by Photoshop Experts Introduction. When using social networks, have you ever encountered a 'fake identity'? Anyone can create a fake profile image using image editing tools, or even using deep learning based generators. If you are interested in. Results for Sightcorp's own solution, the newly minted Deep Learning face detector, on the other hand, returned a 10×10 grid of nearly a full set of 100% face detection. It's a stark differentiation that gave Sightcorp a significant advantage in the calibration of extreme head poses — yet another feather in its already full hat of a solution It uses dlib's new deep learning API to train the detector end-to-end on the very same 4 image dataset used in the HOG version of the example program. Happily, and very much to the surprise of myself and my colleagues, it learns a working face detector from this tiny dataset. Here is the detector run over an image not in the training data: I expected the CNN version of MMOD to inherit the low.

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