A two-stage linear discriminant analysis for face-recognition
نویسندگان
چکیده
0167-8655/$ see front matter 2012 Elsevier B.V. A doi:10.1016/j.patrec.2012.02.001 ⇑ Corresponding author at: Laboratory of DNA In Genome Center, Institute of Medical Science, Univers E-mail addresses: [email protected], sharma A two-stage linear discriminant analysis technique is proposed that utilizes both the null space and range space information of scatter matrices. The technique regularizes both the between-class scatter and within-class scatter matrices to extract the discriminant information. The regularization is conducted in parallel to give two orientation matrices. These orientation matrices are concatenated to form the final orientation matrix. The proposed technique is shown to provide better classification performance on face recognition datasets than the other techniques. 2012 Elsevier B.V. All rights reserved.
منابع مشابه
Supervised Feature Extraction of Face Images for Improvement of Recognition Accuracy
Dimensionality reduction methods transform or select a low dimensional feature space to efficiently represent the original high dimensional feature space of data. Feature reduction techniques are an important step in many pattern recognition problems in different fields especially in analyzing of high dimensional data. Hyperspectral images are acquired by remote sensors and human face images ar...
متن کاملVideo-based face recognition in color space by graph-based discriminant analysis
Video-based face recognition has attracted significant attention in many applications such as media technology, network security, human-machine interfaces, and automatic access control system in the past decade. The usual way for face recognition is based upon the grayscale image produced by combining the three color component images. In this work, we consider grayscale image as well as color s...
متن کاملReal Time Face Tracking and Recognition (RTFTR)
Real Time Face Tracking and Recognition (RTFTR) is a computer vision project that performs the task of locating human faces in a video stream and recognizing those faces by matching them against the database of known faces. A flexible 6 stage processing pathway, for visual data, has been used for the design of RTFTR which permits the study of collective performance of two or more algorithms whe...
متن کاملFace Recognition by Cognitive Discriminant Features
Face recognition is still an active pattern analysis topic. Faces have already been treated as objects or textures, but human face recognition system takes a different approach in face recognition. People refer to faces by their most discriminant features. People usually describe faces in sentences like ``She's snub-nosed'' or ``he's got long nose'' or ``he's got round eyes'' and so like. These...
متن کاملVideo-Based Face Recognition Based On State-Space Model
This paper proposes a video-based framework for face recognition to identify which faces appear in a video sequence. Our basic idea is like a tracking task to track a selection of person candidates over time according to the observing visual features of face images in video frames. Hence, we employ the state-space model to formulate video-based face recognition by dividing this problem into two...
متن کاملAn Improved NN Training Scheme Using Two-Stage LDA Features for Face Recognition
This paper presents a new approach based on a Two-Stage Linear Discriminant Analysis (Two-Stage LDA) and Conjugate Gradient Algorithms (CGAs) for face recognition. A Two-Stage LDA technique is proposed that utilises the null space of the sample covariance matrix as well as using the range space of the between-class scatter matrix to extract discriminant information. Classic Back Propagation (BP...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Pattern Recognition Letters
دوره 33 شماره
صفحات -
تاریخ انتشار 2012