Open AccessSoft biometrics‐based face image retrieval using improved grey wolf optimisation
Author(s) -
Sikkandar Haseena,
Thiyagarajan Revathi
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.0271
Subject(s) - computer science , artificial intelligence , biometrics , face (sociological concept) , convolutional neural network , facial recognition system , pattern recognition (psychology) , feature (linguistics) , computer vision , image (mathematics) , feature extraction , social science , sociology , linguistics , philosophy
Humans often use faces to recognise and identify individuals. Face recognition is one of the important tasks carried out by forensic examiners manually during their investigation, when there is an evidence image/video available from a crime scene. There is a growing demand for face recognition from unconstrained images, which is valuable for criminal investigators in identifying the victims. When an input face image is given to the proposed system, it filters from large scale face dataset to find the top‐k similar faces. Deep convolutional neural network approach is employed to extract important features present in the input face image and improved grey wolf optimisation approach is proposed to select optimal features from the extracted features. It is then preceded by a soft biometric‐based face matcher that helps in retrieving exact face image from the top‐k similar faces matched using approximate nearest neighbour. The performance of the proposed system is evaluated using LFW, CASIA, Multi‐pie and Color‐Feret datasets. The proposed system addresses the challenge of searching face images from a large collection of unconstrained images by incorporating feature retrieval using DCNN and IGWO with soft biometric face matcher in cascaded framework which improves accuracy and reduces computation and retrieval time.