PCA-Based Face Recognition: Similarity Measures and Number of Eigenvectors
This paper examines the performance of face recognition using Principal Component Analysis by (i) varying number of eigenvectors; and (ii) using different similarity measures for classification. We tested 15 similarity measures. ORL database is used for e
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Abstract. This paper examines the performance of face recognition using Principal Component Analysis by (i) varying number of eigenvectors; and (ii) using different similarity measures for classification. We tested 15 similarity measures. ORL database is used for experimentation work which consists of 400 face images. We observed that changing similarity measure causes significant change in the performance. System showed best performance using following distance measures: Cosine, Correlation and City block. Using Cosine similarity measure, we needed to extract lesser images (30 %) in order to achieve cumulative recognition of 100 %. The performance of the system improved with the increasing number of eigenvectors (till roughly 30 % of eigenvectors). After that performance almost stabilized. Some of the worst performers are Standardized Euclidean, Weighted Modified SSE and Weighted Modified Manhattan. Keywords: Biometrics Distance measures
Face recognition
Principal component analysis
1 Introduction There are many types of personal authentication systems and face recognition is one of the active research areas since last several decades. Several methods have been proposed to recognize faces [1–3]. There are two main categories of face recognition methods: feature-based and appearance-based [1]. Using appearance-based methods, a face image of size N N pixels is represented by a vector in N2 dimensional space. Practically, these spaces are too large to perform robust and fast recognition of faces. To solve this problem, dimensionality reduction is done using Principal Component Analysis (PCA) technique. In 1987, PCA was first used to represent face images by Sirovich and Kirby [4]. Turk and Pentland applied PCA to face recognition and presented eigenfaces method in 1991 [5]. We study the effect of 15 similarity measures on the performance of face recognition using PCA. Following characteristics are used to measure the system performance: area above cumulative match characteristics (CMC), rate of recognition and percent of images needed to extract to achieve cumulative recognition of 100 %. © Springer International Publishing Switzerland 2016 A. Campilho and F. Karray (Eds.): ICIAR 2016, LNCS 9730, pp. 69–77, 2016. DOI: 10.1007/978-3-319-41501-7_9
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S.N. Borade and R.R. Deshmukh
Organization of the paper is as follows: In Sect. 2, we present face recognition using PCA technique in detail. Various similarity measures are described in Sect. 3. In Sect. 4, experimental work and the results obtained are presented. Section 5 offers the conclusion.
2 Principal Component Analysis We implemented face recognition using PCA as proposed by Turk and Pentland [5]. Let the gallery set of M face images be Г1, Г2,…, ГM. The average face image of the whole set is defined by W¼
1 XM C i¼1 i M
ð1Þ
Each face image differs from the average face, Ѱ, by the vector /i ¼ Ci W, where i = 1 to M. Find covariance matrix C as C ¼ AAT ; where matrix A ¼ ½/1 /2 ::/M :
ð2Þ
Matrix C is of size N2 by N2. It’s computationally expen
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