Face Recognition Using Haar – Cascade Classifier for Criminal Identification
Senthamizh Selvi. R1, D. Sivakumar2, Sandhya. J. S3, Siva Sowmiya. S4, Ramya. S5, Kanaga Suba Raja.S6
1Senthamizh Selvi. R, Associate Professor, Department of ECE, Easwari Engineering College, Chennai (Tamil Nadu), India.
2D. Sivakumar, Professor, Department of ECE, Easwari Engineering College, Chennai (Tamil Nadu), India.
3Sandhya. J. S, Associate Professor, Department of IT, Easwari Engineering College, Chennai (Tamil Nadu), India.
4Siva Sowmiya. S, U.G. Students, Department of ECE, Easwari Engineering College, Chennai (Tamil Nadu), India.
5Ramya. S, Department of ECE, Easwari Engineering College, Chennai (Tamil Nadu), India.
6Kanaga Suba Raja. S, Department of ECE, Easwari Engineering College, Chennai (Tamil Nadu), India.
Manuscript received on 12 May 2019 | Revised Manuscript received on 19 May 2019 | Manuscript Published on 23 May 2019 | PP: 1871-1876 | Volume-7 Issue-6S5 April 2019 | Retrieval Number: F13360476S519/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Security and Authentication is an imperative part of any industry. In Real time, Human face recognition can be performed in two stages such as, Face detection and Face recognition. This paper implements “Haar-Cascade algorithm” to identify human faces which is organized in Open CV by Python language and”Local binary pattern algorithm” to recognize faces. Collating with other existing algorithms, this classifier produces a high recognition rate even with varying expressions, efficient feature selection and low assortment of false positive features. Haar feature-based cascade classifier system utilizes only 200 features out of 6000 features to yield a recognition rate of 85-95%.
Keywords: Face Recognition, Raspberry-pi, Hear – Cascade, LBPH, Open CV, Criminal Identification, Recognition Rate.
Scope of the Article: Pattern Recognition