2.5 D Facial Analysis via Bio-Inspired Active Appearance Model and Support Vector Machine for Forensic Application

Siti Norul,Nazri Ahmad,Khairul Akram,Fasly Rahim,Mohd Zakree,Zulaiha Othman,Mohammed Hasan

doi:10.14569/ijacsa.2017.080749

Siti Norul, Nazri Ahmad + Show 5 more

Open Access

https://doi.org/10.14569/ijacsa.2017.080749

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Abstract

In this paper, a fully automatic 2.5D facial technique for forensic applications is presented. Feature extraction and classification are fundamental processes in any face identification technique. Two methods for feature extraction and classification are proposed in this paper subsequently. Active Appearance Model (AAM) is one of the familiar feature extraction methods but it has weaknesses in its fitting process. Artificial bee colony (ABC) is a fitting solution due to its fast search ability. However, it has drawback in its neighborhood search. On the other hand, PSO-SVM is one of the most recent classification approaches. However, its performance is weakened by the usage of random values for calculating velocity. To solve the problems, this research is conducted in three phases as follows: the first phase is to propose Maximum Resource Neighborhood Search (MRNS) which is an enhanced ABC algorithm to improve the fitting process in current AAM. Then, Adaptively Accelerated PSO-SVM (AAPSO-SVM) classification technique is proposed, by which the selection of the acceleration coefficient values is done using particle fitness values in finding the optimal parameters of SVM. The proposed methods AAM-MRNS, AAPSO-SVM and the whole 2.5D facial technique are evaluated by comparing them with the other methods using new 2.5D face image data set. Further, a sample of Malaysian criminal real case of CCTV facial investigation suspect has been tested in the proposed technique. Results from the experiment shows that the proposed techniques outperformed the conventional techniques. Furthermore, the 2.5D facial technique is able to recognize a sample of Malaysian criminal case called “Tepuk Bahu” using CCTV facial investigation.

Highlights

The reliability of evidence in a court of law is dependent upon how the evidence is handled, how it is interpreted, and how it is presented
Most of classification techniques use Particle Swam Optimization with Support Vector Machine (PSO-SVM) [22] which employs PSO to find the optimal parameters for SVM, even though its performance has deteriorated by the usage of random values for calculating velocity
The feature extraction stage uses the proposed active appearance model approach based on Maximum Resource Neighborhood Search (MRNS) which is an enhanced Artificial bee colony (ABC) algorithm (AAMMNRS), and the classification stage is based on the proposed acceleration particle swarm optimization (AAPSO)-SVM [23]

Summary

INTRODUCTION

The reliability of evidence in a court of law is dependent upon how the evidence is handled, how it is interpreted, and how it is presented. In forensics, evidence can be explained with Locard’s Exchange Principle, which holds that the perpetrator of a crime will bring something into the crime scene and leave something from it [3] This principle is the foundation of forensic sciences and this extends to the digital forensics, and video forensic is no exception. Face images captured by the CCTV are nonideal due to many factors: pose, illumination, expression, distance and weather [5] These degradations have caused many dead ends to video forensic analysis. The paper highlights on the methodology of 2.5 D Facial Analysis via Bio-Inspired Active Appearance Model and Support Vector Machine It is organized as follows: In Section 2, the discussion on the application of face recognition in digital forensics will be outlined. The overall work of this paper is summarized in the last section

APPLICATION OF FACE RECOGNITION IN DIGITAL FORENSICS

STATE OF THE ART

PROPOSED MODEL

EXPERIMENTAL RESULT

CONCLUSION