Face Detection Using Backpropagation Neural Networks (original) (raw)
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Face Detection and Recognition using Feed Forward Back Propagation Neural Network
— Human face detection and recognition applications present a great interest in the area of computer vision, with various methods and approaches that provide impressive performance. It plays important role in many applications such as video surveillance and face image database management. However, it is difficult to develop a complete robust face detector due to various light conditions, face sizes, face orientations, facial expressions, background and skin colors —In this paper, we present a feed forward back propagation based artificial neural network learning algorithm for recognizing human faces. A facial detection and recognition system has been proposed to recognize registered faces in the database and new faces that are not part of the database. Thus, when capturing a new picture, the essential step is to detect the picture and transfer it to the database where there is a pool of registered faces. Consequently, the system will respond to the new picture by either of the following two actions; first it will start by comparing this new image with the group of registered faces in the database. Accordingly, it responds by either recognizing the new picture and corresponding it with the matching face, or by identifying it as a new face that is not found in the database. The Artificial Neural Network trained by the back-propagation algorithm has 10 inputs, one hidden layer, and one output layer. Experimental results demonstrate successful face detection and recognition using the adopted algorithm.
Face Detection using Neural Network
International Journal of Computer Applications, 2010
This paper describes a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates. As continual research is being conducted in the area of computer vision, one of the most practical applications under vigorous development is in the construction of a robust real-time face detection system. Successfully constructing a real-time face detection system not only implies a system capable of analyzing video streams, but also naturally leads onto the solution to the problems of extremely constraint testing environments. Analyzing a video sequence is the current challenge since faces are constantly in dynamic motion, presenting many different possible rotational and illumination conditions. While solutions to the task of face detection have been presented, detection performances of many systems are heavily dependent upon a strictly constrained environment. The problem of detecting faces under gross variations remains largely uncovered. This paper gives a face detection system which uses an image based neural network to detect face images.
Detection, Segmentation and Recognition of Face and its Features Using Neural Network
Journal of Biosensors & Bioelectronics, 2016
Face detection and recognition has been prevalent with research scholars and diverse approaches have been incorporated till date to serve purpose. The rampant advent of biometric analysis systems, which may be full body scanners, or iris detection and recognition systems and the finger print recognition systems, and surveillance systems deployed for safety and security purposes have contributed to inclination towards same. Advances has been made with frontal view, lateral view of the face or using facial expressions such as anger, happiness and gloominess, still images and video image to be used for detection and recognition. This led to newer methods for face detection and recognition to be introduced in achieving accurate results and economically feasible and extremely secure. Techniques such as Principal Component analysis (PCA), Independent component analysis (ICA), Linear Discriminant Analysis (LDA), have been the predominant ones to be used. But with improvements needed in the previous approaches Neural Networks based recognition was like boon to the industry. It not only enhanced the recognition but also the efficiency of the process. Choosing Backpropagation as the learning method was clearly out of its efficiency to recognize non linear faces with an acceptance ratio of more than 90% and execution time of only few seconds.
Face detection using neural networks and image decomposition
Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290), 2002
In this paper, A new approach to reduce the computation time taken by fast neural nets for the searching process is presented. The principle of divide and conquer strategy is applied through image decomposition. Each image is divided into small in size sub-images and then each one is tested separately using a fast neural network. Compared to conventional and fast neural networks, experimental results show that a speed up ratio is achieved when applying this technique to locate human faces in automatically in cluttered scenes. Furthermore, faster face detection is obtained by using parallel processing techniques to test the resulted sub-images at the same time using the same number of fast neural networks. Moreover, the problem of sub-image centering and normalization in the Fourier space is solved.
Implementation of Neural Network Algorithm for Face Detection Using MATLAB
In this paper, a new approach of face detection system is developed. This system develops the algorithm for computing the accurate measurement of face features. The task of detecting and locating human faces in arbitrary images is complex due to the variability present across human faces, including skin color, pose, expression, position and orientation, and the presence of 'facial furniture' such as glasses or facial hair. In this system, a neural network-based upright frontal face detection system is presented. A retinally connected neural network examines small windows of an image, and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. A straightforward procedure for aligning positive face examples for training was presented. It uses a transformer that converts an image of human face into a feature vector, which will then be compared with the feature vectors of a training set of human faces to classify the image. Some mathematical concepts are used to calculate the distance and angles between feature points. And histogram equalization is used to enhance the selected feature. In this paper, faces are chosen because it can generate the significant features for human face than other techniques. Finally, matching is accomplished by detecting the test photo. For programming and simulation of this system, MATLAB software is applied. The neural network toolbox " nntool " is called from the main function for training system. This research develops a simple face detection system for to provide the security system.
Face recognition has received substantial attention from researches in biometrics, pattern recognition field and computer vision communities. Face recognition can be applied in Security measure at Air ports, Passport verification, Criminals list verification in police department, Visa processing , Verification of Electoral identification and Card Security measure at ATM's. In this paper, a face recognition system for personal identification and verification using Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN) is proposed. This system consists on three basic parts, first: the Face Detection part-which automatically detect human face image using BPNN, second: the various facial features extraction, and the third: face recognition are performed based on Principal Component Analysis (PCA) with BPNN. The dimensionality of face image is reduced by the PCA and the recognition is done by the BPNN for efficient and robust face recognition. This paper also focuses the face database with different sources of variations, especially pose, expression, accessories, lighting and bacgrounds would be used to advance the state-of-the-art face recognition technologies aiming at practical applications
A neural network method for accurate face detection on arbitrary images
ICECS'99. Proceedings of ICECS '99. 6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.99EX357), 1999
In this paper we present a neural detector of frontal faces in gray scale images under arbitrary face size, orientation, facial expression, skin color, lighting conditions and background environment. In a two-level process, a window normalization module reduces the variability of the features and a neural classifier generates multiple face position hypotheses.
Design of Portable Security System Using Face Recognition with Back-Propagation Algorithm and MATLAB
In our globally connected world, threats from various aspects are going at an alarming rate. These are controlling with different security systems such as metal detector, closed circuit cameras, and scanning systems. All these aids are meant to recognize and identify the explosives and others weapons. Here, it is more important to identify the particular person or persons, who were planning to distract the society or particular event. This paper is aimed to design that to control the threats by identifying the suspected people by a simple face recognition technique using simple PC or laptop with the help of scientific software MATLAB and its neural network tool box. In general, all major events are fully securitized with welldeveloped protection systems but only problem with non-major and small events, where security systems are matter of financial issues. So, militants and other destroyers are taking advantage of these situations and creating a panic and terror situations. This paper is also designed like that a PC or laptop with camera can be a face recognition system to identify the suspected peoples and most wanted criminal. By recognizing the people, we can mostly avoid the threats from these people and dangerous situations. Neural network is a science that has been extensively applied to numerous pattern recognition problems such as character recognition, object recognition, and face recognition, where this paper has programmed for face recognition with the back-propagation algorithm and simulated with the software MATLAB and its neural network tool box. Here, the back propagation plays the central operation role to get the key features were extracted from the picture for training the network. Since the major role of the project is mainly focusing on the training of the neural network, already extracted key features of the person’s image from the database were taken for training the back-propagation network. Here, we have taken 7 input units, 6 hidden units, and 4 output units contained back-propagation network. The output unit, 4 output units, generates the 4-bit output which gives the person identity.