On the false rejection ratio of face recognition based on automatic detected feature points

Kazuo Ohzeki, Masahiro Takatsuka, Masaaki Kajihara, Yutaka Hirakawa, Kiyotsugu Sato

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The authors propose a new face recognition system with an evaluation function using feature points. The feature points are detected automatically by Milborrow’s Stasm software. Before recognition, rotation compensation and size normalization are applied to the feature points. The main method is to calculate the squared error between the registered face and the input face as to length of a characteristic pair of feature points on face. The False Rejection Rate (FRR) for the registered and input face of the same person, and the False Acceptance Rate (FAR) for the registered face and a different person’s input face are evaluated. The input is a video sequence. Stable recognition is obtained with small FRR and FAR for the video of a period of 0.5 s.

Original languageEnglish
Pages (from-to)379-384
Number of pages6
JournalPattern Recognition and Image Analysis
Volume26
Issue number2
DOIs
Publication statusPublished - 2016 Apr 1

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Function evaluation
Face recognition
Compensation and Redress

Keywords

  • face recognition
  • feature points
  • individual characteristics
  • normalization
  • rotation compensation

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

Cite this

On the false rejection ratio of face recognition based on automatic detected feature points. / Ohzeki, Kazuo; Takatsuka, Masahiro; Kajihara, Masaaki; Hirakawa, Yutaka; Sato, Kiyotsugu.

In: Pattern Recognition and Image Analysis, Vol. 26, No. 2, 01.04.2016, p. 379-384.

Research output: Contribution to journalArticle

Ohzeki, Kazuo ; Takatsuka, Masahiro ; Kajihara, Masaaki ; Hirakawa, Yutaka ; Sato, Kiyotsugu. / On the false rejection ratio of face recognition based on automatic detected feature points. In: Pattern Recognition and Image Analysis. 2016 ; Vol. 26, No. 2. pp. 379-384.
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