Detection and recognition of face using neural network

Detection and recognition of face using neural network Supervised By: Submitted By: Dr. Nitin Malik Smriti Tikoo 14-ECP-015 Mtech 4th Sem(ECE)

Agenda • Face detection • Face detection algorithms • Viola Jones algorithm • Flowchart • Faces and features detected • Face Recognition and its need. • Back Propagation • Sigmoidal Function • Flowchart • Solution Methodology • Binary Image • Histogram • Neural Network • Results • Conclusion • References • Publications

Face Detection • Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. • It refers to the psychological process by which humans locate and attend to faces in a visual scene. • Face-detection algorithms focus on the detection of frontal human faces. • It is analogous to image detection in which the image of a person is matched bit by bit. • Some facial algorithms identify by doing facial feature extraction , or by analyzing relative position , size and or shape of eyes , cheekbones etc • These features are then used to search images with matching features. Any facial feature changes in the database will invalidate the matching process.

Face Detection Algorithm • The Viola–Jones object detection framework is the first object detection framework to provide competitive object detection rates in real-time proposed in 2001 by Paul Viola and Michael Jones. • Although it can be trained to detect a variety of object classes, it was motivated primarily by the problem of face detection. • This algorithm is implemented in OpenCV as cvHaarDetectObjects().k

Viola Jones Algorithm • The characteristics of Viola–Jones algorithm which make it a good detection algorithm are: • Robust – very high detection rate . • Real time – For practical applications • Face detection only (not recognition) - The goal is to distinguish faces from non-faces (detection is the first step in the recognition process). • The algorithm has four stages: • Haar Feature Selection • Creating an Integral Image • Ada boost Training • Cascading Classifiers

Flow chart depicting the viola Jones procedure INPUT IMAGE HAAR FETURE SELECTION INTEGRAL IMAGE ADABOOST TRAINING CASCADING CLASSIFIERS

Face Recognition and its need • Plays an integral part in human computer interaction. • It is typically used in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems. • Recently, it has also become popular as a commercial identification and marketing tool. • Some facial recognition algo’s identify by doing facial feature detection &extraction , or by analyzing relative position , size and or shape of eyes , cheekbones etc • The skill to identify a face is quite robust despite of large variations in visual stimulus due to changing condition, aging and distractions such as beard , glasses or changes in hairstyle. • Trivial for brain extremely difficult to imitate artificially.

Back Propagation • Backward Propagation of Errors • Most common & popular- for training NN. • Requires known, desired o/p for each i/p in order to calculate the loss function gradient. • Considered supervised learning method. • BP algo consists of 2 propagation: • Forward Propagation : Input is fed through the network to generate propagation's output activations. • Backward propagation: Backward propagation of the propagation's output activations through the neural network using the training pattern target in order to generate the deltas (the difference between the targeted and actual output values) of all output and hidden neurons.

Back Propagation Network Architecture

Continued….. • Gives insights into how changing the weights and biases changes the overall network behavior. • Has a high rate of convergence . • The design of back propagation architecture lies entirely on the type of activation function selected. • The activation function used in this case is sigmoidal activation function.

Sigmoidal Function • A mathematical function having ‘S’ shape . • Often it refers to the special case of logistic function . • S(t)=11+e(-t). • Is a bounded differentiable real function defined for all the real input values and has a positive derivative at each point.

Flow diagram of face recognition

Solution Methodology • Image processing consisting of capturing the facial image of a person. • Face detection and feature detection. • RGB to gray image conversion. • Gray to Binary Conversion of images. • Finding the histogram equivalent of the given binary image. • Neural Network Training and processing procedure where it is trained using neural network fitting tool .

Binary Image • Is a digital image that has only two possible value for each pixel. • Typically the two colors used for a binary image are black and white though any two colors can used . • The color used for the object in the image is the foreground color while the rest of the image is background color. • In the scanning industry it is often referred as bitonal. • Uses im2bw keyword to convert an image to binary.

Histogram • A histogram is a graphical representation of the distribution of numerical data. It is an estimate of the probability distribution of a continuous variable (quantitative variable) and was first introduced by Karl Pearson. • To construct a histogram, the first step is to "bin" the range of values—that is, divide the entire range of values into a series of intervals—and then count how many values fall into each interval. • The bins are usually specified as consecutive, non-overlapping intervals of a variable. The bins (intervals) must be adjacent, and are usually equal size.

Neural Network • To work in neural network one needs to write a command nnstart in the command window. • nftool-Opens the neural network fitting tool GUI. It leads through solving a data fitting problem ,solving it with a two layer feed – forward network trained with Levenberg Marquardt Back Propagation Algo. • nntraintool opens the neural network training GUI.This function can be called to make the training GUI visible before training has occurred, after training if the window has been closed, or just to bring the training GUI to the front. • Network training functions handle all activity within the training window. • To access additional useful plots, related to the current or last network trained, during or after training, click their respective buttons in the training window. • nntraintool close or nntraintool('close') closes the training window.

Neural network Training procedure

Performance Plot of the Referred Journal

Conclusion • In this work it has been shown that if a facial image of a person is given then the network can be able to recognize the face of the person. The whole work is completed through the following steps : • Facial detection is influenced by clarity of the image , colored or black and white image . • It can only support frontal detection of images. • The training does takes a lot of time in order to separate a negative face from a positive face. • Using Adaptive boost algorithm and cascading helps in faster detection. • Facial image without dividing into parts has been applied in the network. • Feed Forward Back Propagation neural network have been used to train, test and validate the network for each part of the image using MATLAB. • Training of each sample is performed up to 8 to 10 times to minimize the mean square error.

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Publications Journal Publications • Smriti Tikoo, Nitin Malik, “Detection of face using Viola Jones algorithm and recognition using Backpropagation neural network”, International Journal of Computer science and Mobile Computing, vol 5, issue no. 5, pg.288-295, May 2016. • Communicated: “Detection, segmentation and recognition of face and its features using neural networks”, International Journal of Advanced Research in Computer and Communication Engineering.

Detection and recognition of face using neural network

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