Face Recognition Using Fuzzy Moments Discriminant Analysis

In this work, an enhanced feature extraction method for holistic face recognition approach of gray intensity still image, namely Fuzzy Moment Discriminant Analysis is used. Which is first, based on Pseudo-Zernike Moments to extract dominant and significant features for each image of enrolled person, then the dimensionality of the moments features vectors is further reduced into discriminant moment features vectors using Linear Discriminant Analysis method, for these vectors the membership degrees in each class have been computed using Fuzzy K-Nearest Neighbor, after that, the membership degrees have been incorporated into the redefinition of the between-classes and within-classes scatter matrices to obtain final features vectors of  known persons. The test image is then compared with the faces enrollment images so that the face which has the minimum Euclidean distance with the test image is labeled with the identity of that image. Keyword: Zernike Moments, LDA, Fuzzy K-Nearest Neighbor

A Multi-Stage Classifier for Face Recognition Undertaken by Coarse-to-fine Strategy

Face recognition has been a very active research area for past two decades due to its widely applications such as identity authentication, airport security and access control, surveillance, and video retrieval systems, etc. Numerous approaches have been proposed for face recognition and considerable successes have been reported [1]. A successful face recognitio

Wavelet based approach for facial expression recognition

Facial expression recognition is one of the most active fields of research. Many facial expression recognition methods have been developed and implemented. Neural networks (NNs) have capability to undertake such pattern recognition tasks. The key factor of the use of NN is based on its characteristics. It is capable in conducting learning and generalizing, non-linear mapping, and parallel computation. Backpropagation neural networks (BPNNs) are the approach methods that mostly used. In this study, BPNNs were used as classifier to categorize facial expression images into seven-class of expressions which are anger, disgust, fear, happiness, sadness, neutral and surprise. For the purpose of feature extraction tasks, three discrete wavelet transforms were used to decompose images, namely Haar wavelet, Daubechies (4) wavelet and Coiflet (1) wavelet. To analyze the proposed method, a facial expression recognition system was built. The proposed method was tested on static images from JAFFE database

Facial Images Improvement in the LBPH Algorithm Using the Histogram Equalization Method

In face recognition research, detecting several parts of the face becomes a necessary part of the study. The main factor in this work is lighting; some obstacles emerge when the low light's intensity falls in the process of face detection because of some conditions, such as weather, season, and sunlight. This study focuses on detecting faces in dim lighting using the Local Binary Pattern Histogram (LBPH) algorithm assisted by the Classifier Method, which is often used in face detection, namely the Haar Cascade Classifier. Furthermore, It will employ the image enhancement method, namely Histogram Equalization (HE), to improve the image source from the webcam. In the evaluation, different light intensities and various head poses affect the accuracy of the method. As a result, The research reaches 88% accuracy for successful face detection. Some factors such as head accessories, hair covering the face, and several parts of the face, like the eye, mouth, and nose that are invisible, should not be extreme

A survey of face detection, extraction and recognition

The goal of this paper is to present a critical survey of existing literatures on human face recognition over the last 4-5 years. Interest and research activities in face recognition have increased significantly over the past few years, especially after the American airliner tragedy on September 11 in 2001. While this growth largely is driven by growing application demands, such as static matching of controlled photographs as in mug shots matching, credit card verification to surveillance video images, identification for law enforcement and authentication for banking and security system access, advances in signal analysis techniques, such as wavelets and neural networks, are also important catalysts. As the number of proposed techniques increases, survey and evaluation becomes important

A Two-Stage Classifier Using SVM and RANSAC for Face Recognition

Abstract-A novel face recognition scheme based on twostage classifier, which includes methods of support vector machine (SVM), and random sample consensus (RANSAC), is proposed in this paper. The whole decision process is undertaken by cascade stages. The first stage with OAO-SVM (one-against-one) method picks out two classes with the least variations to the testing images. From the selected two classes, the second stage with "RANSAC" method is used for a fine match with testing images. A fine class with greatest geometric similarity to testing images is thus produced at second stage. This two-stage face recognition system has been tested on Olivetti Research Laboratory (ORL) databases, and the experimental results give evidence that the proposed approach is superior to the previous approaches based on the single classifier and multi-parallel classifier in recognition accuracy

Gender Classification from Facial Images

Gender classification based on facial images has received increased attention in the computer vision community. In this work, a comprehensive evaluation of state-of-the-art gender classification methods is carried out on publicly available databases and extended to reallife face images, where face detection and face normalization are essential for the success of the system. Next, the possibility of predicting gender from face images acquired in the near-infrared spectrum (NIR) is explored. In this regard, the following two questions are addressed: (a) Can gender be predicted from NIR face images; and (b) Can a gender predictor learned using visible (VIS) images operate successfully on NIR images and vice-versa? The experimental results suggest that NIR face images do have some discriminatory information pertaining to gender, although the degree of discrimination is noticeably lower than that of VIS images. Further, the use of an illumination normalization routine may be essential for facilitating cross-spectral gender prediction. By formulating the problem of gender classification in the framework of both visible and near-infrared images, the guidelines for performing gender classification in a real-world scenario is provided, along with the strengths and weaknesses of each methodology. Finally, the general problem of attribute classification is addressed, where features such as expression, age and ethnicity are derived from a face image