Novel Face Detection Technique

Abstract This paper proposes a classification-based face detection method using Gabor filter features. Considering the desirable characteristics of spatial locality and orientation selectivities of the Gabor filter, we design filters for extracting facial features from the local image. The feature vector based on Gabor filters is used as the input of the classifier, which is

Multiple Moving Object Recognitions in video based on Log Gabor-PCA Approach

Object recognition in the video sequence or images is one of the sub-field of computer vision. Moving object recognition from a video sequence is an appealing topic with applications in various areas such as airport safety, intrusion surveillance, video monitoring, intelligent highway, etc. Moving object recognition is the most challenging task in intelligent video surveillance system. In this regard, many techniques have been proposed based on different methods. Despite of its importance, moving object recognition in complex environments is still far from being completely solved for low resolution videos, foggy videos, and also dim video sequences. All in all, these make it necessary to develop exceedingly robust techniques. This paper introduces multiple moving object recognition in the video sequence based on LoG Gabor-PCA approach and Angle based distance Similarity measures techniques used to recognize the object as a human, vehicle etc. Number of experiments are conducted for indoor and outdoor video sequences of standard datasets and also our own collection of video sequences comprising of partial night vision video sequences. Experimental results show that our proposed approach achieves an excellent recognition rate. Results obtained are satisfactory and competent.Comment: 8,26,conferenc

2D Face Recognition System Based on Selected Gabor Filters and Linear Discriminant Analysis LDA

We present a new approach for face recognition system. The method is based on 2D face image features using subset of non-correlated and Orthogonal Gabor Filters instead of using the whole Gabor Filter Bank, then compressing the output feature vector using Linear Discriminant Analysis (LDA). The face image has been enhanced using multi stage image processing technique to normalize it and compensate for illumination variation. Experimental results show that the proposed system is effective for both dimension reduction and good recognition performance when compared to the complete Gabor filter bank. The system has been tested using CASIA, ORL and Cropped YaleB 2D face images Databases and achieved average recognition rate of 98.9 %

FPGA Implementation Of A Novel Robust Facial Expression Recognition Algorithm

A facial expression recognition system depicts about state of mind of a particular by showing their emotions, thus has potential application in various field of human computer interaction (HCI) such as to aid autistic children, robot control and many more. This work presents a robust and hardware efficient algorithm for facial expression recognition which gives very high rate of accuracy. Broadly, human facial expression has been categorized in seven categories, named as anger, disgust, fear, happy, sad, surprise with basic neutral emotion. The process of emotion recognition starts with the image capturing, detecting the face in the image of which emotion has to recognize, extracting robust and unique features of image which makes categorization efficient and classification of features for one of the above mentioned emotion categories. Face detection out of an image is done using existing Bayesian discriminating feature method. An algorithm is proposed for facial expression recognition, integrating Gabor filter bank and its features for feature extraction, statistical modelling which uses principal component analysis PCA and conditional density function for modelling of features and extended Bayes classifier for multi-class classification of emotion in a detected face. The multi class classification strategic has been applied based on highest value of log likelihood after training different emotions class. Robust features are extracted using Gabor filter with 8 frequency and 8 orientations. FPGA implementation of the extended Bayesian classifier is done on Xilinx10.1, VirtexIIpro FPGA using CORDIC unit for trigonometric functions. Facial expression images from JAFFE database have been used for training as well as testing. Very high accuracy (96.73 %) of emotion recognition has been obtained with proposed method

Entropy Projection Curved Gabor with Random Forest and SVM for Face Recognition

In this work, we propose a workflow for face recognition under occlusion using the entropy projection from the curved Gabor filter, and create a representative and compact features vector that describes a face. Despite the reduced vector obtained by the entropy projection, it still presents opportunity for further dimensionality reduction. Therefore, we use a Random Forest classifier as an attribute selector, providing a 97% reduction of the original vector while keeping suitable accuracy. A set of experiments using three public image databases: AR Face, Extended Yale B with occlusion and FERET illustrates the proposed methodology, evaluated using the SVM classifier. The results obtained in the experiments show promising results when compared to the available approaches in the literature, obtaining 98.05% accuracy for the complete AR Face, 97.26% for FERET and 81.66% with Yale with 50% occlusion