• Lingasamy G
• Kishore
• Vishnu Vardhan
• Vijayakumar

Detecting human beings accurately in a visual surveillance system is crucial for diverse application areas including abnormal event detection, human gait characterization, congestion analysis, person identification, gender classification and fall detection for elderly people. The first step of the detection process is to detect an object which is in motion. Object detection could be performed using background subtraction, filtering and feature extraction techniques. Once detected, object could be classified as a human being using shape-based, texture-based or motion-based features. A comprehensive review with comparisons on available techniques for detecting human beings in surveillance videos is presented . The characteristics of few benchmark datasets as well as the future research directions on human detection have also been discussed.

Face recognition, face detection, Haar Detection, Raspberry Pi Module, Histogram Equalization, local binary pattern, ROI extraction.

"Feature Based Face Recognition for Identification of Persons using Raspberry PI", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.119-126, May-2019, Available :http://www.jetir.org/papers/JETIRBJ06024.pdf

"Feature Based Face Recognition for Identification of Persons using Raspberry PI", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp119-126, May-2019, Available at : http://www.jetir.org/papers/JETIRBJ06024.pdf