DocumentCode :
3057661
Title :
Bag-of-Words Vector Quantization Based Face Identification
Author :
Liu, Di ; Sun, Dong-mei ; Qiu, Zheng-Ding
Author_Institution :
Inst. of Inf. Sci., Beijing Jiaotong Univ., Beijing, China
Volume :
2
fYear :
2009
fDate :
22-24 May 2009
Firstpage :
29
Lastpage :
33
Abstract :
This paper investigates the possibility that uses Scale-Invariance Feature Transform (SIFT) feature for face identification. However, it is impossible to employ these SIFT keys,i.e. feature vectors, for identification directly, due to the space incompatible of such SIFT keys. To this end, the Bag-of-words (Bow) vector quantization introduced from scene or text classification is conducted for unifying them. And a novel distance, Cauchy-Schwartz Inequality Distance (CSID), is performed for determining which cluster each keypoint of image belongs to after quantization, in order to compose a histogram vector as our feature. To summarize, this approach solves the problem of space incompatible and avoids a high computation that gives rises to a "dimensional curse". The experiment shows a reasonable result using SVM classifier by ORL database.
Keywords :
face recognition; feature extraction; pattern clustering; statistical analysis; transforms; vector quantisation; Cauchy-Schwartz inequality distance; bag-of-words histogram vector quantization; face identification; scale-invariance feature transform; scene classification; text classification; Fingerprint recognition; Histograms; Kernel; Layout; Object recognition; Spatial databases; Support vector machine classification; Support vector machines; Text categorization; Vector quantization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electronic Commerce and Security, 2009. ISECS '09. Second International Symposium on
Conference_Location :
Nanchang
Print_ISBN :
978-0-7695-3643-9
Type :
conf
DOI :
10.1109/ISECS.2009.15
Filename :
5209820
Link To Document :
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