[4] Castrillón Marco, Déniz Oscar, Guerra Cayetano, and Hernández Mario, " ENCARA2: Real-time detection of multiple faces at different resolutions in video streams" . In Journal of Visual Communication and Image Representation, 2007 (18) 2: pp. 130-140.
WIDER FACE dataset is a face detection benchmark dataset, of which images are selected from the publicly available WIDER dataset. We choose 32,203 images and label 393,703 faces with a high degree of variability in scale, pose and occlusion as depicted in the sample images. WIDER FACE dataset is organized based on 61 event classes. For each event class, we randomly select 40%/10%/50% data as training, validation and testing sets. We adopt the same evaluation metric employed in the PASCAL VOC dataset. Similar to MALF and Caltech datasets, we do not release bounding box ground truth for the test images. Users are required to submit final prediction files, which we shall proceed to evaluate.
face detection matlab code free download 15
Please contact us to evaluate your detection results. An evaluation server will be available soon. The detection result for each image should be a text file, with the same name of the image. The detection results are organized by the event categories. For example, if the directory of a testing image is "./0--Parade/0_Parade_marchingband_1_5.jpg", the detection result should be writtern in the text file in "./0--Parade/0_Parade_marchingband_1_5.txt". The detection output is expected in the follwing format:.........Each text file should contain 1 row per detected bounding box, in the format "[left, top, width, height, score]". Please see the output example files and the README if the above descriptions are unclear.
MatConvNet is a MATLAB toolbox implementing Convolutional NeuralNetworks (CNNs) for computer vision applications. It is simple,efficient, and can run and learn state-of-the-art CNNs. Manypre-trained CNNs for image classification, segmentation, facerecognition, and text detection are available.
When we project 3-D objects on a 2-D screen, we need to detect the faces that are hidden on 2D.Back-Face detection, also known as Plane Equation method, is an object space method in which objects and parts of objects are compared to find out the visible surfaces. Let us consider a triangular surface that whose visibility needs to decided. The idea is to check if the triangle will be facing away from the viewer or not. If it does so, discard it for the current frame and move onto the next one. Each surface has a normal vector. If this normal vector is pointing in the direction of the center of projection, then it is a front face and can be seen by the viewer. If this normal vector is pointing away from the center of projection, then it is a back face and can not be seen by the viewer.
The Back-face detection method is very simple. For the left-handed system, if the Z component of the normal vector is positive, then it is a back face. If the Z component of the vector is negative, then it is a front face.
Before I discuss face recognition, it's important to discuss another related technique: face detection. As the name implies, face detection is a technique that identifies human faces in a digital image. Face detection is a relatively mature technology - remember back in the good old days of your digital camera when you looked through the viewfinder? You saw rectangles surrounding the faces of the people in the viewfinder.
For face detection, one of the most famous algorithms is known as the Viola-Jones Face Detection technique, commonly known as Haar cascades. Haar cascades are from long before deep learning was popular and is one of the most commonly used techniques for detecting faces.
Fortunately, without needing to know how Haar cascades work, OpenCV can perform face detection out of the box using a pre-trained Haar cascade, along with other Haar cascades for recognizing other objects. The list of predefined Haar cascades is available on GitHub at
Next, create a file named face_detection.py and populate it with the code shown in Listing 1. Then, download the haarcascade_frontalface_default.xml file and save it into the same directory as the face_detection.py file.
Now that you've learned how to detect faces, you are ready to tackle the bigger challenge of recognizing faces! Face detection is a technique for detecting faces in an image and face recognition is a technique for recognizing faces in an image. Compared to face detection, face recognition is a much more complicated process and is an area of much interest to researchers, who are always looking to improve the accuracy of the recognition.
VGGFace refers to a series of models developed for face recognition. It was developed by the Visual Geometry Group (hence its VGG name) at the University of Oxford. The models were trained on a dataset comprised mainly of celebrities, public figures, actors, and politicians. Their names were extracted from the Internet Movie Data Base (IMDB) celebrity list based on their gender, popularity, pose, illumination, ethnicity, and profession (actors, athletes, politicians). The images of these names were fetched from Google Image Search, and multiple images for each name were downloaded, vetted by humans, and then labelled for training.
For the example here, I'm going to use the SENet model. When you run the above code snippets, the weights for the trained model will be downloaded and stored in the /.keras/models/vggface folder. Here's the size (in bytes) of the weights downloaded for each model:
The loaded face label is a dictionary containing the mapping of integer values to the names of the people that you have trained. The above code snippet converted that dictionary into a list that looks like this:
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The American Multiracial Face Database contains 110 faces (smiling and neutral expression poses) with mixed-race heritage and accompanying ratings of those faces by naive observers that are freely available to academic researchers. The faces were rated on attractiveness, emotional expression, racial ambiguity, masculinity, racial group membership(s), gender group membership(s), warmth, competence, dominance, and trustworthiness.
The dataset contains images of people collected from the web by typing common given names into Google Image Search. The coordinates of the eyes, the nose and the center of the mouth for each frontal face are provided in a ground truth file. This information can be used to align and crop the human faces or as a ground truth for a face detection algorithm. The dataset has 10,524 human faces of various resolutions and in different settings, e.g. portrait images, groups of people, etc. Profile faces or very low-resolution faces are not labeled.
Face Research Toolkit: A free and open-source toolkit of three-dimensional models and software to study face perception. Contains 8 manipulatable facial expression models.
Citation: Hays, J. S., Wong, C., & Soto, F. (2020). FaReT: A free and open-source toolkit of three-dimensional models and software to study face perception. Behavior Research Methods, 5.(6), 2604-2622.
Major FeaturesDocumentationUnlike a lot of open source projects, this one provides complete and precise documentation for every class and function. There are also debugging modes that check the documented preconditions for functions. When this is enabled it will catch the vast majority of bugs caused by calling functions incorrectly or using objects in an incorrect manner.
Lots of example programs are provided
I consider the documentation to be the most important part of the library. So if you find anything that isn't documented, isn't clear, or has out of date documentation, tell me and I will fix it.
High Quality Portable CodeGood unit test coverage. The ratio of unit test lines of code to library lines of code is about 1 to 4.
The library is tested regularly on MS Windows, Linux, and Mac OS X systems. However, it should work on any POSIX system and has been used on Solaris, HPUX, and the BSDs.
No other packages are required to use the library. Only APIs that are provided by an out of the box OS are needed.
There is no installation or configure step needed before you can use the library. See the How to compile page for details.
All operating system specific code is isolated inside the OS abstraction layers which are kept as small as possible. The rest of the library is either layered on top of the OS abstraction layers or is pure ISO standard C++.
Machine Learning AlgorithmsDeep Learning
Conventional SMO based Support Vector Machines for classification and regression
Reduced-rank methods for large-scale classification and regression
Relevance vector machines for classification and regression
General purpose multiclass classification tools
A Multiclass SVM
A tool for solving the optimization problem associated with structural support vector machines.
Structural SVM tools for sequence labeling
Structural SVM tools for solving assignment problems
Structural SVM tools for object detection in images as well as more powerful (but slower) deep learning tools for object detection.
Structural SVM tools for labeling nodes in graphs
A large-scale SVM-Rank implementation
An online kernel RLS regression algorithm
An online SVM classification algorithm
Semidefinite Metric Learning
An online kernelized centroid estimator/novelty detector and offline support vector one-class classification
Clustering algorithms: linear or kernel k-means, Chinese Whispers, and Newman clustering.
Radial Basis Function Networks
Multi layer perceptrons
Numerical AlgorithmsA fast matrix object implemented using the expression templates technique and capable of using BLAS and LAPACK libraries when available.
Numerous linear algebra and mathematical operations are defined for the matrix object such as the singular value decomposition, transpose, trig functions, etc.
General purpose unconstrained non-linear optimization algorithms using the conjugate gradient, BFGS, and L-BFGS techniques
Levenberg-Marquardt for solving non-linear least squares problems
Box-constrained derivative-free optimization via the BOBYQA algorithm
An implementation of the Optimized Cutting Plane Algorithm
Several quadratic program solvers
Combinatorial optimization tools for solving optimal assignment and min cut/max flow problems as well as the CKY algorithm for finding the most probable parse tree
A big integer object
A random number object
Graphical Model Inference AlgorithmsJoin tree algorithm for exact inference in a Bayesian network.
Gibbs sampler markov chain monte carlo algorithm for approximate inference in a Bayesian network.
Routines for performing MAP inference in chain-structured, Potts, or general factor graphs.
Image ProcessingRoutines for reading and writing common image formats.
Automatic color space conversion between various pixel types
Common image operations such as edge finding and morphological operations
Implementations of the SURF, HOG, and FHOG feature extraction algorithms.
Tools for detecting objects in images including frontal face detection and object pose estimation.
High quality face recognition
ThreadingThe library provides a portable and simple threading API
A message passing pipe for inter-thread and inter-process communication
A timer object capable of generating events that are regularly spaced in time
Threaded objects
Threaded functions
Parallel for loops
A thread_pool with support for futures
NetworkingThe library provides a portable and simple TCP sockets API
An object to help you make TCP based servers
iostream and streambuf objects that enables TCP sockets to interoperate with the C++ iostreams library
A simple HTTP server object you can use to embed a web server into your applications
A message passing pipe for inter-thread and inter-process communication
A tool used to implement algorithms using the Bulk Synchronous Parallel (BSP) computing model
Graphical User InterfacesThe library provides a portable and simple core GUI API
Implemented on top of the core GUI API are numerous widgets
Unlike many other GUI toolkits, the entire dlib GUI toolkit is threadsafe
Data Compression and Integrity AlgorithmsA CRC 32 object
MD5 functions
Various abstracted objects representing parts of data compression algorithms. Many forms of the PPM algorithm are included.
TestingA thread safe logger object styled after the popular Java logger log4j
A modular unit testing framework
Various assert macros useful for testing preconditions
General UtilitiesA type-safe object to convert between big and little endian byte orderings
A command line parser with the ability to parse and validate command lines with various types of arguments and options
An XML parser
An object that can perform base64 conversions
Many container classes
Serialization support
Many memory manager objects that implement different memory pooling strategies
A tool that lets you easily call C++ from MATLAB
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