ABSTRACT

In practice, identification of criminal in Nigeria is done through thumbprint identification. However, this type of identification is constrained as most of criminal nowadays getting cleverer not to leave their thumbprint on the scene. With the advent of security technology, cameras especially CCTV have been installed in many public and private areas to provide surveillance activities. The footage of the CCTV can be used to identify suspects on scene. However, because of limited software developed to automatically detect the similarity between photo in the footage and recorded photo of criminals, the law enforce thumbprint identification. In this study, an automated facial recognition system for criminal database was proposed using known Principal Component Analysis approach. This system will be able to detect face and recognize face automatically. This will help the law enforcements to detect or recognize suspect of the case if no thumbprint present on the scene. The results show that about 80% of input photo can be matched with the template data.

CHAPTER ONE

INTRODUCTION

Over the years, a lot of security approaches have been developed that help in keeping confidential data secured and limiting the chances of a security breach. Face recognition which is one of the few biometric methods that possess the merits of both high accuracy and low intrusiveness is a computer program that uses a person’s face to automatically identify and verify the person from a digital image or a video frame from a video source [1, 2, 3]. It compares selected facial features from the image and a face database or it can also be a hardware which used to authenticate a person. This technology is a widely used biometrics system for authentication, authorization, verification and identification. A lot of company has been using face recognition in their security cameras, access controls and many more. Facebook has been using face recognition in their website for the purpose of creating a digital profile for the people using their website. In developed countries, the law enforcement create face database to be used with their face recognition system to compare any suspect with the database. In other hand, in Malaysia, most cases are investigated by using thumbprint identification to identify any suspect for the case. However, because of unlimited knowledge through internet usage, most criminals are aware of thumbprint identification. Therefore, they become more cautious of leaving thumbprint by wearing gloves except for non-premeditated crimes. This paper to propose a facial recognition system for a criminal database where the identification of the suspect is done by face matched rather than thumbprint matched.

The objective of this study is two-fold:

1. Matching a face with available database accurately.

2. Applying principal component analysis for finding distinguishable features from many images to get the similarity for the target image. The remaining of this study is structured as follows. Next section discusses on related concepts of this study and relevant previous works, design and development describes the whole processes of system development, result and discussion highlights the outcomes and advantages, and final section outlines conclusion and future work.

OVERVIEW OF FACE RECOGNITION SYSTEMS

Face Recognition for Criminal Identification is a face recognition system in which the security expert will input an image of the person in question inside the system and the system will first preprocess the image which will cause unwanted elements such as noise to be removed from the image. After that, the system will then classify the image based on its landmarks for example, the distance between the eyes, the length of the jaw line, etc. Then, the system will run a search through the database to find its perfect match and display the output. This work is focusing on implementing the system for criminal identification. Current practice of thumbprint identification which is simple and easy to be implemented can be challenge by the use of latent thumbprint and sometimes cannot be acquired from the crime scene. The criminals have become cleverer and normally be very careful in leaving any thumbprint on the scene. This system encompassed face database and an image processing algorithm to match the face feed with faces stored in the database. There are two parts vital to the success of this system; detection and recognition. A face detection is one of the most important steps in a face recognition system and can be classified into four principle categories; knowledge based, feature invariant, template matching and appearance-based methods [4]. In recognition, two stages are required; training process and evaluation process. In a training process, the algorithm is fed samples of the images to be learned and a distinct model for each image is determined while in an evaluation process, a model of a newly acquired test image is compared against all existing models in the database. Then the near corresponding model is acquired to determine whether the recognition is triggered [5]. In this stage, a statistical procedure, Principal Component Analysis (PCA) is used to on a collection of face images to form a set of basis features, which is called a set of eigenfaces. Any human face can be considered to be a combination of these standard face.

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