Dimensionality reduced local directional number pattern for face recognition

Face recognition and facial expression recognition using local patterns is the order of the day. Local directional number pattern (LDNP) is one of the prominent descriptor for face recognition. LDNP assigns a 3 bit code for each pixel in the image. The resultant LDNP labeled image is divided into re...

Full description

Saved in:
Bibliographic Details
Published in:Journal of ambient intelligence and humanized computing 2018-02, Vol.9 (1), p.95-103
Main Authors: Ramalingam, Srinivasa Perumal, Paturu Venkata Subbu Sita Rama, Chandra Mouli
Format: Article
Language:English
Subjects:
Artificial Intelligence
Biometrics
Computational Intelligence
Discriminant analysis
Engineering
Face recognition
Facial recognition technology
Histograms
Lighting
Neighborhoods
Original Research
Pattern recognition
Physiology
Robotics and Automation
User Interfaces and Human Computer Interaction
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Face recognition and facial expression recognition using local patterns is the order of the day. Local directional number pattern (LDNP) is one of the prominent descriptor for face recognition. LDNP assigns a 3 bit code for each pixel in the image. The resultant LDNP labeled image is divided into regions to form histogram based descriptor. The histogram bins of all the regions are concatenated to form the final descriptor. In contrast to LDNP, a dimensionality reduced local directional number pattern (DR-LDNP) is proposed in this paper. The proposed descriptor computes single code for each block. This is done by X-ORing of the LDNP codes obtained in a single block. During the process, restructuring of the patterns is done by slightly modifying the LDNP coding pattern constraints. The resultant DR-LDNP descriptor outperforms the existing methods. The experimentation is carried out on standard databases like FERET, YALE, ORL, Cohn–Kannade and JAFFEE and obtained good recognition rates compared to other methods.
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-016-0408-x