Weihua Wang

Bio: Weihua Wang is an academic researcher from Chongqing University. The author has contributed to research in topics: Eigenface & 3D single-object recognition. The author has an hindex of 1, co-authored 1 publications receiving 9 citations.

A Gray-Scale Face Recognition Approach

Abstract: Face recognition has been a focus in research for the last couple of decades because of its wide potential applications and its importance to meet the security needs of today's world. However, the network is very complex and therefore it is difficult to train. To reduce complexity, neural network is often applied to the pattern recognition phase rather than to the feature extraction phase. In order to cope with such complication and find out the true invariant for recognition, researchers have developed various recognition algorithms. In this paper, we give a new face recognition algorithm based on gray-scale. The paper shows the readers the basic principle of this face recognition algorithm, the implement of the new face recognition approach, the advantage of the new algorithm, and shows higher recognition rate than eigenface algorithm in the same experiment conditions according to our practices.

A survey of human face detection

Abstract: Human face detection problem, originated in face recogniti on research as its first step to locate faces in the images, has recently been intensively researched as an independent problem because of its applications in many different fields including security access control, advanced human and computer interaction.

Face Recognition System Based on Different Artificial Neural Networks Models and Training Algorithms

Abstract: Face recognition is one of the biometric methods that is used to identify any given face image using the main features of this face. In this research, a face recognition system was suggested based on four Artificial Neural Network (ANN) models separately: feed forward backpropagation neural network (FFBPNN), cascade forward backpropagation neural network (CFBPNN), function fitting neural network (FitNet) and pattern recognition neural network (PatternNet). Each model was constructed separately with 7 layers (input layer, 5 hidden layers each with 15 hidden units and output layer). Six ANN training algorithms (TRAINLM, TRAINBFG, TRAINBR, TRAINCGF, TRAINGD, and TRAINGD) were used to train each model separately. Many experiments were conducted for each one of the four models based on 6 different training algorithms. The performance results of these models were compared according to mean square error and recognition rate to identify the best ANN model. The results showed that the PatternNet model was the best model used. Finally, comparisons between the used training algorithms were performed. Comparison results showed that TrainLM was the best training algorithm for the face recognition system.

Integrated system for monitoring and recognizing students during class session

Abstract: In this paper we propose a new student attendance system based on biometric authentication protocol. This system is basically using the face detection and the recognition protocols to facilitate checking students’ attendance in the classroom. In the proposed system, the classroom’s camera is capturing the students’ photo, directly the face detection and recognition processes will be implemented to produce the instructor attendance report. Actually, this system is more efficient than others student attendance methods since the detection and the recognition are considered to be the best and fastest method for biometric attendance system. Regarding to the students and instructor sides, the system is working without any preparation and with no more effort.

Comparative research of the common face detection methods

Abstract: Face detection is an important part of the face recognition, in this article the common face detection algorithms are summarized and classified, and several representative face detection algorithm are compared, respectively, the principle, advantages and disadvantages of these methods is described and finally the development trends and challenges of face detection method are presented.

Analysis of Random Local Descriptors in Face Recognition

Abstract: This article describes and analyzes the new feature extraction technique, Random Local Descriptor (RLD), that is used for the Permutation Coding Neural Classifier (PCNC), and compares it with Local Binary Pattern (LBP-based) feature extraction. The paper presents a model of face feature detection using local descriptors, and describes an improvement on the PCNC for the recognition of plane rotated and small displaced face images, as applied to three databases, i.e., ORL, FRAV3D and FEI. All databases are described along with the recognition results that were obtained. We also include a comparison of our classifier with the Support Vector Machine (SVM) and Iterative Closest Point (ICP). The ORL database was selected to compare our RLDs with LBP-based algorithms. The PCNC with the RLDs demonstrated the best recognition rate, i.e., 97.49%, in comparison with 90.49% for LBPs. For the FEI image database, we obtained the best recognition rate, i.e., 93.57%, in comparison with 66.74% for LBPs. Using the RLDs and rotating the original images for FRAV3D, we improved the recognition rate by decreasing by approximately twice the number of errors. In addition, we analyzed the influence of different RLD parameters on the quality of facial recognition.