Personal identification system based on multi biometric depending on cuckoo search algorithm

Abstract

In modern devices, many personal identification systems are used using various biometrics to confirm the identity of an individual and identify him for several purposes. Some of these essential biometrics are used in this paper to help identify a person while attaining social distance because of the widespread epidemics. The features of the face, eyes, nose, and finally, the features of the mouth are used in this article. The work begins by detecting the parts of multibiometric from the input images using Viola-Jones face detection algorithm with a modification to it then segment them. After that, various initial treatment processes begin which helps clarify them to facilitate subsequent operations. Also, a Histogram of oriented gradient method(HOG) is used to extract the significant features of those image segments. The extracted features from these segments are entered into the developed cuckoo search algorithm(DCSA), the best image segment within the used dataset is searched for similar in terms of characteristics to the entered image segment. The work has also been developed so that the system is executed on two cores using parallel processing technology to utilize the processor as much as possible and reduce the time it takes to implement the system and identify the person concerned. A high identification rate has been reached, reaching 99.25%, and overall speed up 1.40323 sec relative to serial execution.

Different Biometrical Features for Detecting Human Intrusion Using Artificial Intelligence: Literature Review

“…In another research [51], the face, eyes, mouth and nose were used to identify people using the intelligent cuckoo search algorithm, Fig. Here in table 2 some of these properties of last five years:…”

Developing Viola Jones' algorithm for detecting and tracking a human face in video file

“…One of these studies is the use of the neural aggregation network algorithm [12], convolutional neural networks (CNN), and deep convolutional neural networks, the following is a comparison between these studies and the proposed method as shown in Table 2. This research aims to discover an effective method for determining the area of the human face in video sequences by using the important improvements made to Viola Jones' face detection algorithm [17] that determines the facial area in digital images while not allowing the loss of the facial area to occur. This method can be used in many important applications such as browsing the video, identifying the face of people in the videos for crime detection, and indexing the video.…”

Identify tooth cone beam computed tomography based on contourlet particle swarm optimization

“…Also, the wrong acceptance rate (6) in this system was 5%, meaning that any radiograph that does not exist in the radiograph dataset of the systemis not accepted. And this was done according to the following formula [27], [28]:…”