An Improved Local Binary Patterns Histograms Technique for Face Recognition for Real Time Applications
Akshit Anand1, Vikrant Jha2, Lavanya Sharma3
1Akshit Anand, BCA Graduate, Amity Institute of Information Technology, Amity University, Noida (U.P), India.
2Vikrant Jha, BCA Graduate, Amity Institute of Information Technology, Amity University, Noida (U.P), India.
3Lavanya Sharma, Ph.D. Graduate, Amity Institute of Information Technology, Amity University, Noida (U.P), India.
Manuscript received on 05 August 2019 | Revised Manuscript received on 28 August 2019 | Manuscript Published on 05 September 2019 | PP: 524-529 | Volume-8 Issue-2S7 July 2019 | Retrieval Number: B10980782S719/2019©BEIESP | DOI: 10.35940/ijrte.B1098.0782S719
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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: Recently, face recognition and its applications has been considered as one of the image analysis most successful applications, especially over the past several years. Face Recognition is a unique system that can be used by using unique facial features for identification or verification of a person from a digital image. In a face recognition system, there are many technique that can be used. This paper provides an efficient of the Local Binary Patterns Histograms (LBPH) based technique provided by OpenCV library which is implemented in Python programming language which is well suitable for realistic scenarios. In this paper we also provide visual qualitative outcome with existing algorithm (Haar-cascade classifier and Local Binary Patterns Histograms (LBPH)). As a result, the proposed technique outperform better in terms of visual qualitative analysis.
Keywords: Face Recognition, Face Detection, Local Binary Patterns Histograms, Open CV, Haar-Cascade Classifier, Python.
Scope of the Article: Pattern Recognition