An efficient color compensation scheme for skin color segmentation

2002-2003 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Face Recognition using Fused Diagonal and Matrix Features

Face recognition with less information availability in terms of the number of image samples is a challenging task A simple and efficient method for face recognition is proposed in this paper to address small sample size problem and rotation variation of input images The robert s operator is used as edge detection method to elicit borders to crop the facial part and then all cropped images are resized to a uniform 50 50 size to complete the preprocessing step Preprocessed test images are rotated in different angles to check the robustness of proposed algorithm All preprocessed images are partitioned into one hundred 5 5 equal size parts The matrix 2-norm infinite norm trace and rank are elicited for each of 5 5 part and respectively averaged to yield on hundred matrix features Another one hundred diagonal features are extracted by applying a 3 3 mask on each image Final one hundred features are obtained by fusing averaged matrix and diogonal features Euclidian distance measure is used for comparision of database and query image features The results are comparitively better on three publically availabe datasets compared to existing method

Comparative Analysis of advanced Face Recognition Techniques

ABSTRACT: This project entitled "Comparative analysis of advanced Face Recognition Techniques", it is based on fuzzy c means clustering and associated sub neural network. It deals with the face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. In this paper, it represents a method for face recognition base on similar neural networks. Neural networks (NNs) have been widely used in various fields. However, the computing effectiveness decreases rapidly if the scale of the NN increases. In this paper, a new method of face recognition based on fuzzy clustering and parallel NNs is proposed. The face patterns are divided into several small-scale neural networks based on fuzzy clustering and they are combine to obtain the recognition result. The facial feature vector was compared by PCA and LDA methods. In particular, the proposed method achieved 98.75% recognition accuracy for 240 patterns of 20 registrants and a 99.58% rejection rate for 240 patterns of 20 nonregistrants. Experimental results show that the performance of our new face-recognition method is better than those of the LDA based face recognition system

3D Model Based Pose Invariant Face Recognition from a Single Frontal View

This paper proposes a 3D model based pose invariant face recognition method that can recognize a face of a large rotation angle from its single nearly frontal view. The proposed method achieves the goal by using an analytic-to-holistic approach and a novel algorithm for estimation of ear points. Firstly, the proposed method achieves facial feature detection, in which an edge map based algorithm is developed to detect the ear points. Based on the detected facial feature points 3D face models are computed and used to achieve pose estimation. Then we reconstruct the facial feature points' locations and synthesize facial feature templates in frontal view using computed face models and estimated poses. Finally, the proposed method achieves face recognition by corresponding template matching and corresponding geometric feature matching. Experimental results show that the proposed face recognition method is robust for pose variations including both seesaw rotations and sidespin rotations

People objects : 3-D modeling of heads in real-time

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 1998.Includes bibliographical references (p. 54-59).by Thomas E. Slowe.S.M