Automated Students Attendance System

The Automated Students' Attendance System is a system that takes the attendance of students in a class automatically. The system aims to improve the current attendance system that is done manually. This work presents the computerized system of automated students' attendance system to implement genetic algorithms in a face recognition system. The extraction of face template particularly the T-zone (symmetrical between the eyes, nose and mouth) is performed based on face detection using specific HSV colour space ranges followed by template matching. Two types of templates are used; one on edge detection and another on the intensity plane in YIQ colour space. Face recognition with genetic algorithms will be performed to achieve an automated students' attendance system. With the existence of this attendance system, the occurrence of truancy could be reduced tremendously

Watershed Segmentation for Face Detection Using Artificial Neural Network

In a face image containing objects sometimes face has a color similar to the background color or objects that are nearby. This causes the system to detect any objects in the face in an image. This study wants to try to overcome these problems. The approach used in this study is a dynamic image segmentation. The segmentation will produce region-region are then used as input for the neural network. From the experiments conducted, the method used is good enough to detect faces. The results showed that the approach used in this study can detect all of the data that had trained, while for the data that has not been trained detection rate reached 70%

Recognizing Facial Expression using PCA and Genetic Algorithm

This paper presents an efficient method of recognition of facial expressions in a video. The works proposes highly efficient facial expression recognition system using PCA optimized by Genetic Algorithm .Reduced computational time and comparable efficiency in terms of its ability to recognize correctly are the benchmarks of this work. Video sequences contain more information than still images hence are in the research subject now-a-days and have much more activities during the expression actions. We use PCA, a statistical method to reduce the dimensionality and are used to extract features with the help of covariance analysis to generate Eigen –components of the images. The Eigen-components as a feature input is optimized by Genetic algorithm to reduce the computation cost

Face Recognition Using Eigen-Wavelet-Face Method

This work is concerned with investigation for face recognition methods suitable for different environments.  Eigenface method based on Principle Component Analysis (PCA) is modified here by operating on wavelet transformed face image to extract recognition features in a hybrid scheme called Eigen-Wavelet-Face aiming to improve the recognition rate and/or complexity.  Four standard face image databases are used in the work. The databases have different parameters related to size, type, expressions, lighting, orientation, and the number of images per person. The original Eigenface and suggested Discrete Wavelet Transform (DWT) face recognition methods are also used in the work for the sake of comparison. The results showed that the Eigenface method is a time consuming due to its huge computations.  For databases having large number of training images and variations, the proposed hybrid method achieved 100% recognition rate, while for those databases with smaller training sets DWT method obtained the best recognition rate of 95% under favorite condition. Key words: Face recognition, Eigenface, PCA, DWT, Feature extraction.

Automated Students Attendance System

The Automated Students' Attendance System is a system that takes the attendance of students in a class automatically. The system aims to improve the current attendance system that is done manually. This work presents the computerized system of automated students' attendance system to implement genetic algorithms in a face recognition system. The extraction of face template particularly the T-zone (symmetrical between the eyes, nose and mouth) is performed based on face detection using specific HSV colour space ranges followed by template matching. Two types of templates are used; one on edge detection and another on the intensity plane in YIQ colour space. Face recognition with genetic algorithms will be performed to achieve an automated students' attendance system. With the existence of this attendance system, the occurrence of truancy could be reduced tremendously

Face Recognition Using Completed Local Ternary Pattern (CLTP) Texture Descriptor

Nowadays, face recognition becomes one of the important topics in the computer vision and image processing area. This is due to its importance where can be used in many applications. The main key in the face recognition is how to extract distinguishable features from the image to perform high recognition accuracy.  Local binary pattern (LBP) and many of its variants used as texture features in many of face recognition systems. Although LBP performed well in many fields, it is sensitive to noise, and different patterns of LBP may classify into the same class that reduces its discriminating property. Completed Local Ternary Pattern (CLTP) is one of the new proposed texture features to overcome the drawbacks of the LBP. The CLTP outperformed LBP and some of its variants in many fields such as texture, scene, and event image classification.  In this study, we study and investigate the performance of CLTP operator for face recognition task. The Japanese Female Facial Expression (JAFFE), and FEI face databases are used in the experiments. In the experimental results, CLTP outperformed some previous texture descriptors and achieves higher classification rate for face recognition task which has reached up 99.38% and 85.22% in JAFFE and FEI, respectively

Heterogeneous Techniques used in Face Recognition: A Survey

Face Recognition has become one of the important areas of research in computer vision. Human Communication is a combination of both verbal and non-verbal. For interaction in the society, face serve as the primary canvas used to express distinct emotions non-verbally. The face of one person provides the most important natural means of communication. In this paper, we will discuss the various works done in the area of face recognition where focus is on intelligent approaches like PCA, LDA, DFLD, SVD, GA etc. In the current trend, combination of these existing techniques are being taken into consideration and are discussed in this paper.Keywords: Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Genetic Algorithm (GA), Direct Fractional LDA (DFLD