Face recognition system using neural network with Gabor and discrete wavelet transform parameterization

Variations in lighting conditions, pose and expression make face recognition an even more challenging and difficult task. This paper presents a face recognition approach by using image enhancement and Gabor wavelets transformation. In face recognition, image preprocessing is the key step since it is important to features extract and recognition. Logarithm transformation and normalization are performed in face images captured under various lighting conditions for face recognition. This involves convolving a face image with a series of Gabor wavelets at different scales, locations, and orientations and extracting features from Gabor filtered images. Significant improvements are also observed when the preprocessing and Gabor filtered images are used for feature extraction instead of the original images. The approach achieves 94.4% recognition accuracy using only 160 features of a face image. Experimental results show that the proposed approach improves face recognition performance using this scheme when training and testing on images captured under variable illumination and expression.

Face recognition technique nowadays is emerging as the most significant and challenging aspects in terms of security for identification of images in various fields viz. banking, police records, biometric etc. other than an individual's thumb and documented identification proofs. Till date for efficient net banking to be initiated, one has to provide the appropriate user name and password for purpose of authentication. This project introduces a vehicle to take a step forward in easy and more reliable authentication of an individual by providing Face Image along with User Name and Password to the system. In this an individual's face is identified by biometric authentication support with which, only a person whose account is, can access it. However while transferring this sensitive data of user image, from client machine to bank server it has to be protected from hackers and intruders from manhandling it, hence it is transferred using covert communication called Wavelet Decomposition based steganography. As face images are affected by different expressions, poses, occlusions, illuminations and aging over a period of time and it differs from the same person than those from different ones is the main difficult task in face recognition. Whenever image information is jointly co-ordinated in three aspects viz. image space, scale and orientation domains they carry much higher clues than seen in each domain individually. In the proposed method combination of Local Binary Pattern (LBP) and Gabor features are used to increase the face recognition performance significantly to compare individual's face presentations. Hence face recognition and representation of Gabor faces are done using E-GV-LBP and CMI-LDA based feature recognition method. Gabor faces uses space, scale and orientation to support accurate face recognition, making net banking easier, authentic, reliable and user friendly.

With the rapid growth in multimedia contents, among such content face recognition has got much attention especially in past few years. Face as an object consists of distinct features for detection; therefore, it remains most challenging research area for scholars in the field of computer vision and image processing. In this survey paper, we have tried to address most endeavoring face features such as pose invariance, aging, illuminations and partial occlusion. They are considered to be indispensable factors in face recognition system when realized over facial images. This paper also studies state of the art face detection techniques, approaches, viz. Eigen face, Artificial Neural Networks (ANN), Support Vector Machines (SVM), Principal Component Analysis (PCA), Independent Component Analysis (ICA), Gabor Wavelets, Elastic Bunch Graph Matching, 3D morphable Model and Hidden Markov Models. In addition to the aforementioned works, we have mentioned different testing face databases which include AT & T (ORL), AR, FERET, LFW, YTF, and Yale, respectively for results analysis. However, aim of this research is to provide comprehensive literature review over face recognition along with its applications. And after in depth discussion, some of the major findings are given in conclusion.

Recognition of human faces is a very important task in many applications such as authentication and surveillance. An efficient face recognition system with face image representation using averaged wavelet and wavelet packet coefficients, Discriminative Common Vector (DCV) and modified Local Binary Patterns (LBP) and recognition using radial basis function (RBF) network is presented. Face images are decomposed by 2-level wavelet and wavelet packet transformation. The discriminative common vectors are obtained for averaged wavelet. The new proposed LBP operator is applied on the obtained DCV and also applied on averaged wavelet packet coefficients of all the samples of a class. The histogram values obtained from the LBP are recognized using RBF network. The proposed work is tested on three face databases such as Olivetti Oracle Research Lab (ORL), Japanese Female Facial Expression (JAFFE) and Essex face database. The proposed method results in good recognition rates along with less training time because of the extracted discriminant input from the preprocessing steps involved in the proposed work.

Human age classification via face images becoming an interesting research area because of potential applications in the field of computer vision such as Age Specific Human Computer Interaction (ASHCI), biometrics, security and surveillance, etc. In this paper, a novel method for human age classification using facial skin analysis for aging feature extraction and Multi-class Support Vector Machine (M-SVM) for age classification is proposed to classify the face images into four age groups. Facial skin analysis consists of skin texture analysis and wrinkle analysis. Gabor wavelet is used to analyze the facial skin textural changes with age progression. Wrinkle analysis detects the wrinkle density changes at particular regions on face image with age progression. The performance evaluation of proposed age classification system is carried out by using face images from PAL face database. In this paper, the performance of M-SVM classifier is compared with the performance of Artificial Neural Network (ANN) classifier for the task of human age classification using Gabor wavelet and wrinkle analysis. The result analysis concludes that the best age classification accuracy of 93.61% is achieved by using proposed age classification system and M-SVM is an efficient classifier than the ANN classifier for the task of human age classification in combination with Gabor wavelet and wrinkle analysis.

Face recognition is a process by which the identity of a person is determined from the face images stored in a face database. Face recognition is one of the most successful applications of image analysis. In the present scenario, face recognition plays a major role in commercial and law enforcement applications, such as surveillance system, passport, security, personal information accesses, human machine interaction, etc. At present, very reliable methods of biometric personal identification exist. In face recognition, a feature vector usually represents the salient characteristics that best describe a face image. However, these characteristics vary quite substantially while looking into a face image from different directions. This chapter addresses this issue by means of image fusion and presents a comprehensive study of different image fusion techniques for face recognition. Image fusion is done between the original captured image and its true/partial diagonal images.

Image set based face recognition has recently become a popular topic as it has better performance than single image based face recognition. However, preprocessing is needed to remove the effects of some adverse conditions such as different pose angles, illumination, and expression differences within the set. One of the most effective preprocessing to improve the face recognition rate is face frontalization. Face frontalization is defined as the artificial acquisition of a face image with a different pose angle to a frontal pose. It has been observed that this process increases the face recognition performance. In this paper, image set based face recognition was performed by applying face frontalization to all images in the sets. Firstly, the faces in the IJBA database were frontalized by using the Rotate and Render hybrid frontalization method, which is based on a Three-Dimensional and Generative Adversial Network. Then, discriminative convex classifier is used for set based face recognition. In face recognition experiments, when the frontalized IJBA database and its non-frontalized version were compared, it was observed that the accuracy of face recognition increased with the frontalized face images.

In this paper we describe a multi-modal ear and face biometric system. The system is comprised of two components: a 3D ear recognition component and a 2D face recognition component. For the 3D ear recognition, a series of frames is extracted from a video clip and the region of interest (i.e., ear) in each frame is independently reconstructed in 3D using Shape From Shading. The resulting 3D models are then registered using the iterative closest point algorithm. We iteratively consider each model in the series as a reference model and calculate the similarity between the reference model and every model in the series using a similarity cost function. Cross validation is performed to assess the relative fidelity of each 3D model. The model that demonstrates the greatest overall similarity is determined to be the most stable 3D model and is subsequently enrolled in the database. For the 2D face recognition, a set of facial landmarks is extracted from frontal facial images using the Active Shape Model. Then, the response of facial images to a series of Gabor filters at the locations of facial landmarks are calculated. The Gabor features (attributes) are stored in the database as the face model for recognition. The similarity between the Gabor features of a probe facial image and the reference models are utilized to determine the best match. The match scores of the ear recognition and face recognition modalities are fused to boost the overall recognition rate of the system. Experiments are conducted using a gallery set of 402 video clips and a probe of 60 video clips (images). As a result, a rank-one identification rate of 100% was achieved using the weighted sum technique for fusion.

Up to today, face recognition is an open research subject in biometrie identification. In this paper we propose a method for face recognition based on the dependence structure of Gabor wavelet. As a useful tool, Gabor wavelets are widely used in image processing field. The strong dependencies exist in the domain of Gabor wavelets due to Gabor filters are not nonorthogonal. We have found that the dependence structure of Gabor wavelets can be used as the feature of face image, and hence it can be used to face recognition. In the proposed method, Gaussian copula is employed to describe the dependence structure of Gabor wavelets. Experiments of face recognition show the proposed method obviously improves the representation performance of Gabor wavelets, and is robust to low resolution face images.

Face detection is the first step in the face recognition system. The Neural network as well as Gabor wavelet based face detection is proposed in this paper. The proposed system applies neural network algorithm to make the slanting face upright and Gabor wavelet is used to extract the invariable features from the selected window. The principal component analysis is applied to identify the person from the data base. The Proposed system gives very high detection efficiency with low number of false alarms. We have shown the results on different test sets with different degrees of variability of the face patterns. In this paper, we present a new approach for determining the orientation of face in the selected window and then use this information to rotate the face upright

Face recognition from the real data, capture images, sensor images and database images is challenging problem due to the wide variation of face appearances, illumination effect and the complexity of the image background. Face recognition is one of the most effective and relevant applications of image processing and biometric systems. In this paper we are discussing the face recognition methods, algorithms proposed by many researchers using artificial neural networks (ANN) which have been used in the field of image processing and pattern recognition. How ANN will used for the face recognition system and how it is effective than another methods will also discuss in this paper. There are many ANN proposed methods which give overview face recognition using ANN. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included, and also the performance analysis of different ANN approach and algorithm is analysing in this research study.

Quaternion-based improved LPP method for color face recognition

The facial image analysis for classifying human age has a vital role in Image processing, Pattern recognition, Computer vision, Cognitive science and Forensic science. The various computational and mathematical models, for classifying facial age includes Principal Component Analysis (PCA) and Wavelet Transforms and Local Binary Pattern (LBP). A more sophisticated method is introduced to improve the performance of the system by decomposing the face image using 2-level linear wavelet transforms and classifying the human age group using Artificial Neural Network. This approach needs normalizing the facial image at first and then extracting the face features using linear wavelet transforms. The distance of the features is measured using Euclidean distance and given as input to Adaptive Resonance Theory (ART). The network is trained with an own dataset consisting of 70 facial images of various age group. The goal of the proposed work is to classify the human age group into four categories as Child, Adolescence, Adult and Senior Adult.

In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which combines both global and local discriminative features. In this method, global features are extracted from whole face images by Fourier transform and local features are extracted from some spatially partitioned image patches by Gabor wavelet transform. After this, multiple classifiers are obtained by applying Fisher Discriminant Analysis on global Fourier features and local patches of Gabor features. All these classifiers are combined to form a hierarchical ensemble by sum rule. We evaluated the proposed method using Face Recognition Grand Challenge (FRGC) experimental protocols and database known as the largest data sets available. Experimental results on FRGC version 2.0 data set have shown that the proposed method achieves a verification rate of 86%, while the best reported was 76%.

A novel Gabor-Kernel face recognition method is proposed in this paper. This involves convolving a face image with a series of Gabor wavelets at different scales, locations, and orientations and extracting features from resulting Gabor filtered images. kernel discriminant analysis (KDDA) is then applied to the feature vectors for dimension reduction as well as class separability enhancement. A database of 600 frontal-view face images from the FERET face database is used to test the method. Experimental results demonstrate the advantage of KDDA over other Kernel methods such as kernel principal component analysis (KPCA) and general discriminant analysis (GDA). Significant improvements are also observed when features are extracted from Gabor filtered images instead of the original images. A 94% accuracy has been observed for the novel Gabor + KDDA method on the FERET database using a simple classifier, which could be further improved by employing a more complex classifier and distance measurer.