This paper is published in Volume-5, Issue-3, 2019
Area
Image Processing
Author
Archana Naik, Rohan Basukala, Santosh Tiwari, Tanka Prasad Tiwari, Prajwal Deep Bhandari, Asha H. V.
Org/Univ
Nitte Meenakshi Institute of Technology, Bengaluru, Karnataka, India
Pub. Date
28 June, 2019
Paper ID
V5I3-1920
Publisher
Keywords
Cascade face recognition, Face feature extraction, Haar, OpenCV

Citationsacebook

IEEE
Archana Naik, Rohan Basukala, Santosh Tiwari, Tanka Prasad Tiwari, Prajwal Deep Bhandari, Asha H. V.. Criminal identification using facial recognition, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Archana Naik, Rohan Basukala, Santosh Tiwari, Tanka Prasad Tiwari, Prajwal Deep Bhandari, Asha H. V. (2019). Criminal identification using facial recognition. International Journal of Advance Research, Ideas and Innovations in Technology, 5(3) www.IJARIIT.com.

MLA
Archana Naik, Rohan Basukala, Santosh Tiwari, Tanka Prasad Tiwari, Prajwal Deep Bhandari, Asha H. V.. "Criminal identification using facial recognition." International Journal of Advance Research, Ideas and Innovations in Technology 5.3 (2019). www.IJARIIT.com.

Abstract

individualistic characters of the human face can be extracted by face recognition. The human face detection and recognition finds a major role in the application as video surveillance, face image database management. Face recognition is a simple and agile biometric technology. This technology uses the most obvious human identifier of the face. The face recognition finds its application in security, health care, criminal identification, places where human recognition is the necessity. With the advancement in technology, the extracting features of the human face become simpler. This paper discusses a different algorithm to recognize the human face. The purpose is to identify the criminal face and retrieve the information stored in the database for the identified criminal. The process is categorized into two major steps. First, the face is extracted from the image, distinguishing factors in the face are extracted and stored in the database. The second step is to compare the resultant image with the existing image and return the data related to that image from the database.