Time bound Adaptive Genetic Algorithm based face recognition (original) (raw)

Genetic Algorithm based Human Face Recognition

This paper proposes a novel algorithm for recognizing human faces using Genetic Algorithms. This proposed method consists of three major phase namely, face representation, face detection and face detection. In Addition, a comparative study is made using Principle Component Analysis and Linear Discriminant Analysis using the commonly used face databases such as Essex Face Database-Face94, Indian Face Database [16], Yale Database, FACE 1999 [18] and UMIST Face Database [19]. The proposed method produces better results than PCA and LDA for one sample per person. For Face 1999, which contained images with different background, the recognition rate is not quite good. But for all the other databases the test produces more than 90% recognition accuracy rate. From the result obtained it is observed that the proposed system does not need more number of images per person. Here the same recognition rate is achieved in around 5 images per person instead of around 10 images per person in basic P...

Face Recognition System Using Genetic Algorithm

Procedia Computer Science, 2016

Face recognition is one of the most challenging aspect in the field of image analysis. Face recognition has been a topic of active research since the 1980's, proposing solutions to several practical problems. Face recognition is probably the biometric method that is used to identify people mainly from their faces. However, the recognition process used by the human brain for identifying faces is very challenging. In this paper, a Genetic Algorithm (GA) based approach is proposed for face recognition. The proposed algorithm recognizes an unknown image by comparing it with the known training images stored in the database and gives information regarding the person recognized. The proposed algorithm is then compared with other known face recognition algorithms viz: Principal Component Analysis(PCA) and Linear Discriminate Analysis (LDA) algorithms. It has been observed that the recognition rate of the proposed algorithm is better.

A Novel Face Recognition Approach Based on Genetic Algorithm Optimization

Studies in Informatics and Control

In the field of image processing and recognition, discrete cosine transform (DCT) and principal component analysis (PCA) are two widely used techniques. In this paper we present a face recognition approach based on them. Feature selection (FS) is a global optimization problem in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. It is the most important step that affects the performance of a face recognition system. Genetic Algorithms (GA), one of the most recent techniques in the field of feature selection, are a type of evolutionary algorithms that can be used also to solve this issue. The application of a GA in the resolution of a problem requires the coding of the potential solutions to this problem in finite bit chains in order to constitute the chromosomes coming from a population formed by candidate points. The aim is to find a selective function allowing good discrimination between chromosomes and to define the genetic operators that will be used. In this sense, this approach seeks to develop a system of face recognition using Genetic Algorithm and a DCT-PCA combination for feature selection and dimensionality reduction, to be applied to an archive of images of human faces. The proposed approach is applied on various Face Databases. Experimental results demonstrate the effectiveness of this approach compared to state of the art in face recognition.

Face Recognition System Genetic with PCA and LDA

Face recognition is one of the most challenging aspects in the field of image analysis. Face recognition has been a topic of active research since the 1980's, proposing solutions to several practical problems. Face recognition is probably the biometric method that is used to identify people mainly from their faces. However, the recognition process used by the human brain for identifying faces is very challenging. In this paper, a Genetic Algorithm (GA) based approach is proposed for face recognition. The proposed algorithm recognizes an unknown image by comparing it with the known training images stored in the database and gives information regarding the person recognized. The proposed algorithm is then compared with other known face recognition algorithms viz: Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA) algorithms. It has been observed that the recognition rate of the proposed algorithm is better.

Face Recognition using Genetic Algorithm and Neural Networks

This article deals with the combinations basics of Genetic Algorithm (GA) and Back Propagation Neural Networks (BPNN) and their applications in Pattern Recognition or for Face Recognition problems. Images have a huge information and characteristics quantities. Until today, a complete efficient mechanism to extract these characteristics in an automatic way is yet not possible. Referring to facial images, its detection in an image is a problem that requires a meticulous investigation due to its high complexity. Here we should investigate the aspects of genetic in face recognition. Genetic Algorithms (GA's) are characterized as one search technique inspired by Darwin Evolutionist Theory. Genetic Algorithm is efficient in reducing computation time for a huge heapspace. Face recognition from a very huge Heap-space is a time consuming task hence genetic algorithm based approach is used to recognize the unidentified -image within a short span of time. BPNN can be viewed as computing models inspired by the structure and function of the biological neural network. See that the training process does not have a single call to a training function, but the network was trained several times on various input ideal and noisy images, the images which contents face. In this case training a network on different sets of noisy images forced the network to learn how to deal with noise, a common problem in the real world. These models are expected to deal with problem solving in a manner different from conventional computing. A distinction is made between pattern and data to emphasize the need for developing pattern processing systems to address pattern recognition tasks.

Feature Selection Using Genetic Algorithm for Face Recognition Based on PCA, Wavelet and SVM

International Journal on Electrical Engineering and Informatics, 2014

Many events, such as terrorist attacks, exposed serious weaknesses in most sophisticated security systems. So it is necessary to improve security data systems based on the body or behavioral characteristics, called biometrics. With the growing interest in the development of human and computer interface and biometric identification, human face recognition has become an active research area. Face recognition offers several advantages over other biometric methods. Nowadays Principal Component Analysis (PCA) has been widely adopted for the face recognition algorithm. Yet still, PCA has limitations such as poor discriminatory power and large computational load. This paper proposed a novel algorithm for face recognition in which a low frequency component of the wavelet is used for PCA representation. Best features of PCA are selected using the genetic algorithm (GA). Support vector machine (SVM) and nearest neighbor classifier (ND) are used for classification. Classification accuracy is examined by changing number of training images, number of features and kernel function. Results are evaluated on ORL, FERET, Yale and YaleB databases. Experiments showed that proposed method gives a better recognition rate than other popular methods.

Human Face Classification using Genetic Algorithm

International Journal of Advanced Computer Science and Applications , 2016

The paper presents a precise scheme for the development of a human face classification system based human emotion using the genetic algorithm (GA). The main focus is to detect the human face and its facial features and classify the human face based on emotion, but not the interest of face recognition. This research proposed to combine the genetic algorithm and neural network (GANN) for classification approach. There are two way for combining genetic algorithm and neural networks, such as supportive approach and collaborative approach. This research proposed the supportive approach to developing an emotion-based classification system. The proposed system received frontal face image of human as input pattern and detected face and its facial feature regions, such as, mouth (or lip), nose, and eyes. By the analysis of human face, it is seen that most of the emotional changes of the face occurs on eyes and lip. Therefore, two facial feature regions (such as lip and eyes) have been used for emotion-based classification. The GA has been used to optimize the facial features and finally the neural network has been used to classify facial features. To justify the effectiveness of the system, several images were tested. The achievement of this research is higher accuracy rate (about 96.42%) for human frontal face classification based on emotion.

A Review on Face Recognition Algorithms

2017

Face recognition has been challenging and interesting area in real time applications. Face recognition is a form of biometric identification that relies on data acquired from the face of an individual. A large number of face recognition along with their modifications, have been developed during the past decades. Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. In real world applications, it is desirable to have a stand-alone, embedded facerecognition system. The reason is that such systems provide a higher level of robustness,hardware optimization, and ease of integration. In this paper an attempt is made to review a wide range of methods used for face recognition comprehensively. This include PCA, ICA, LDA, SVM, Gabor wavelet soft computing tool like ANN for recognition, LBP and various hybrid combination ...

An Efficient Face Recognition Technique Using PCA and Artificial Neural Network

2014

Face recognition is one of the biometric tool for authentication and verification. It is having both research and practical relevance. . Face recognition, it not only makes hackers virtually impossible to steal one's "password", but also increases the userfriendliness in human-computer interaction. Facial recognition technology (FRT) has emerged as an attractive solution to address many contemporary needs for identification and the verification of identity claims. A facial recognition based verification system can further be deemed a computer application for automatically identifying or verifying a person in a digital image.The two common approaches employed for face recognition are analytic (local features based) and holistic (global features based) approaches with acceptable success rates. In this paper, we present a hybrid features based face recognition technique using principal component analysis technique.Principal component analysisis used to compute global feat...

OPTIMIZED PCA BASED FACE RECOGNITION

IJRISAT Journal, 2021

In this paper an algorithm to solve the problem of automatic face recognition is presented. The novelty of the algorithm is the ability to combine the principal component analysis (PCA) with Modified Particle Swarm Optimization (MPSO) to improve the execution time and to obtain better face recognition results. The efficiency of face recognition system is imp measure the similarity of an input face compared with a database of faces. The use of the fitness function helps to obtain more accurate results in a faster way. The results obtained are excellent even when the system w A comparison of the results obtained with the algorithm without MPSO versus the algorithm using MPSO is also presented. The algorithm is also implemented on the colored images of the human faces.