scholarly journals An Accurate Facial Component Detection Using Gabor Filter

2017 ◽  
Vol 6 (3) ◽  
pp. 287-294
Author(s):  
K. Sudhakar ◽  
P. Nithyanandam

Face detection is a critical task to be resolved in a variety of applications. Since faces include various expressions it becomes a difficult task to detect the exact output. Face detection not only play a main role in personal identification but also in various fields which includes but not limited to image processing, pattern recognition, graphics and other application areas. The proposed system performs the face detection and facial components using Gabor filter. The results show accurate detection of facial components

Author(s):  
Shihab Hamad Khaleefah ◽  
Salama A. Mostafa ◽  
Aida Mustapha ◽  
Noor Azah Samsudin ◽  
Mohammad Faidzul Nasrudin ◽  
...  

With the dramatic expansion of image information nowadays, image processing and computer visions are playing a significant role in terms of several applications such as image classification, image segmentation, pattern recognition, and image retrieval. One of the important features that have been used in many image applications is texture. The texture is the characteristic of a set of pixels that formed the image. Therefore, analyzing such texture would have a significant impact on segmenting the image or detecting important portions of such image. This paper aims to overview the feature extraction and description techniques depicted in the literature to characterize regions for images. In particular, two of popular descriptors will be examined including Local Binary Pattern (LBP) and Gabor Filter. The key characteristic behind such investigation lies in how the features of an image would contribute toward the process of recognition and image classification. In this regard, an extensive discussion is provided on both LBP and Gabor descriptors along with the efforts that have been intended to combine them. The reason behind investigating these descriptors is that they are considered the most common local and global descriptors used in the literature. The overall aim of this survey is to show current trends on using, modifying and adapting these descriptors in the domain of image processing.


2019 ◽  
Vol 8 (1) ◽  
pp. 239-245 ◽  
Author(s):  
Shamsul J. Elias ◽  
Shahirah Mohamed Hatim ◽  
Nur Anisah Hassan ◽  
Lily Marlia Abd Latif ◽  
R. Badlishah Ahmad ◽  
...  

Attendance is important for university students. However, generic way of taking attendance in universities may include various problems. Hence, a face recognition system for attendance taking is one way to combat the problem. This paper will present an automated system that will automatically saves student’s attendance into the database using face recognition method. The paper will elaborate on student attendance system, image processing, face detection and face recognition. The face detection part will be done by using viola-jones algorithm method while the face recognition part will be carried on by using local binary pattern (LBP) method. The system will ensure that the attendance taking process will be faster and more accurate.


Author(s):  
Siti Nurmaini ◽  
Ahmad Zarkasi ◽  
Deris Stiawan ◽  
Bhakti Yudho Suprapto ◽  
Sri Desy Siswanti ◽  
...  

In terms of movement, mobile robots are equipped with various navigation techniques. One of the navigation techniques used is facial pattern recognition. But Mobile robot hardware usually uses embedded platforms which have limited resources. In this study, a new navigation technique is proposed by combining a face detection system with a ram-based artificial neural network. This technique will divide the face detection area into five frame areas, namely top, bottom, right, left, and neutral. In this technique, the face detection area is divided into five frame areas, namely top, bottom, right, left, and neutral. The value of each detection area will be grouped into the ram discriminator. Then a training and testing process will be carried out to determine which detection value is closest to the true value, which value will be compared with the output value in the output pattern so that the winning discriminator is obtained which is used as the navigation value. In testing 63 face samples for the Upper and Lower frame areas, resulting in an accuracy rate of 95%, then for the Right and Left frame areas, the resulting accuracy rate is 93%. In the process of testing the ram-based neural network algorithm pattern, the efficiency of memory capacity in ram, the discriminator is 50%, assuming a 16-bit input pattern to 8 bits. While the execution time of the input vector until the winner of the class is under milliseconds (ms).


Author(s):  
Mr. Shubham Ingole

This article describes the technique of real-time face detection, mask detection, and vacant seat available in the vehicle. There are so many technologies for finding seat availability in the vehicle. But image processing technology is very popular today. Face detection is part of image processing. It is used to find the face of a human being in a certain area. Face detection is used in many applications, such as facial recognition, people tracking or photography. In this paper, the face detection technique is used to detect the vacant seat availability in the vehicle and also to detect whether the passenger wear the mask on his face or not. The webcam is installed in the vehicle and connected with the Raspberry Pi 3 model B. When the vehicle leaves the station, the webcam will capture images of the passengers in the seating area. The webcam will be mounted on the vehicle. The images will be adjusted and enhanced to reduce noise made by the software application. The system obtains the maximum number of passengers in the vehicle that processes the images and then calculates the availability of seats in the vehicle. In covid-19 situation mask detection is necessary. so this system also used to detect the mask on face.


2019 ◽  
Vol 8 (4) ◽  
pp. 4803-4807

One of the most difficult tasks faced by the visually impaired students is identification of people. The rise in the field of image processing and the development of algorithms such as the face detection algorithm, face recognition algorithm gives motivation to develop devices that can assist the visually impaired. In this research, we represent the design and implementation of a facial recognition system for the visually impaired by using image processing. The device developed consists of a programmed raspberry pi hardware. The data is fed into the device in the form of images. The images are preprocessed and then the input image captured is processed inside the raspberry pi module using KNN algorithm, The face is recognized and the name is fed into text to speech conversion module. The visually impaired student will easily recognize the person before him using the device. Experiment results show high face detection accuracy and promising face recognition accuracy in suitable conditions. The device is built in such a way to improve cognition, interaction and communication of visually impaired students in schools and colleges. This system eliminates the need of a bulk computer since it employs a handy device with high processing power and reduced costs.


Personal identification is very vital in this digital era for simpler mobile phone unlocking to criminal identification in the scene of crime. There are various methods of personal identification ranging from non-invasive methods of presence of moles in the visible parts of the body to the invasive DNA karyotyping. Other in the spectrum being fingerprinting, lip print, foot print, tongue print, palate print etc. As age advances there might be slight variations in finger print, ear biometric etc, where as in iris the amount of pigmentation might vary but the pattern remains almost same from birth to death, unless otherwise there is any injury to the iris which is very remote. Iris pattern recognition is a non-invasive method of biometric identification. Iris architecture is not only complex but also unique to an individual. In this article a methodology is been proposed to match iris pattern.


Author(s):  
Chandan R

Image processing automated attendance system is the system in which easiest way to record the attendance for organization .This system is based on the face detection and face recognition algorithms. For this we make use of “Image Processing” using “MATLAB”. The concept of this paper is to provide real time attendance of students in a class to the faculty’s data base. Automatically detects the student using the web camera and only detect the facial part of that particular image and the image undergoes the various techniques and will compare with reference image, Later the attendance of the student is updated .Thus with the help of this system time will be saved and it is so convenient to record the attendance at any time throughout the day.


Technology has been playing a vital role in this world, where the work and the work place become digitalized. The paper reviews on monitoring the attendance using image processing, which involves face detection, labeling the detected face, training a classifier based on labeled dataset, and face recognition. Former methods on monitoring the attendance includes signing the attendance registry, fingerprint detection and barcode scanning where delinquency may occur. To prevail over and to take the technology to subsequent level image processing has been incorporated. Proposed system employs, capturing of the face in various dimensions, labeling of the captured images that is stored in the database for training and testing phase. Using the gathered data the machine is trained to recognize the face to provide access to the employees or students in the organization. The final phase is to take the attendance and maintain the record on attending hours using face recognition technique in which the input image of the employees or students is given.


Author(s):  
Richa Singh ◽  
Mayank Vatsa ◽  
Phalguni Gupta

The modern information age gives rise to various challenges, such as organization of society and its security. In the context of organization of society, security has become an important challenge. Because of the increased importance of security and organization, identification and authentication methods have developed into a key technology in various areas, such as entrance control in buildings, access control for automatic teller machines, or in the prominent field of criminal investigation. Identity verification techniques such as keys, cards, passwords, and PIN are widely used security applications. However, passwords or keys may often be forgotten, disclosed, changed, or stolen. Biometrics is an identity verification technique which is being used nowadays and is more reliable, compared to traditional techniques. Biometrics means “life measurement,” but here, the term is associated with the unique characteristics of an individual. Biometrics is thus defined as the “automated methods of identifying or authenticating the identity of a living person, based on physiological or behavioral characteristics.” Physiological characteristics include features such as face, fingerprint, and iris. Behavioral characteristics include signature, gait, and voice. This method of identity verification is preferred over traditional passwords and PIN-based methods for various reasons, such as (Jain, Bolle, & Pankanti, 1999; Jain, Ross, & Prabhakar, 2004): • The person to be identified is required to be physically present for the identity verification. • Identification based on biometric techniques obviates the need to remember a password or carry a token. • It cannot be misplaced or forgotten. Biometrics is essentially a multi-disciplinary area of research, which includes fields like pattern recognition image processing, computer vision, soft computing, and artificial intelligence. For example, face image is captured by a digital camera, which is preprocessed using image enhancement algorithms, and then facial information is extracted and matched. During this process, image processing techniques are used to enhance the face image and pattern recognition, and soft computing techniques are used to extract and match facial features. A biometric system can be either an identification system or a verification (authentication) system, depending on the application. Identification and verification are defined as (Jain et al., 1999, 2004; Ross, Nandakumar, & Jain, 2006): • Identification–One to Many: Identification involves determining a person’s identity by searching through the database for a match. For example, identification is performed in a watch list to find if the query image matches with any of the images in the watch list. • Verification–One to One: Verification involves determining if the identity which the person is claiming is correct or not. Examples of verification include access to an ATM, it can be obtained by matching the features of the individual with the features of the claimed identity in the database. It is not required to perform match with complete database. In this article, we present an overview of the biometric systems and different types of biometric modalities. The next section describes various components of biometric systems, and the third section briefly describes the characteristics of biometric systems. The fourth section provides an overview of different unimodal and multimodal biometric systems. In the fifth section, we have discussed different measures used to evaluate the performance of biometric systems. Finally, we discuss research issues and future directions of biometrics in the last section.


Author(s):  
Dzmitry Tsishkou ◽  
Liming Chen ◽  
Eugeny Bovbel

This work presents a method of semi-automatic ground truth annotation for benchmarking of face detection in video. We aim to illustrate the solution to the issue where an image processing and pattern recognition expert is able to label and annotate facial patterns in video sequences at the rate of 7500 frames per hour. We extend these ideas to the semi-automatic face annotation methodology, where all object patterns are categorized into 4 classes in order to increase flexibility of evaluation results analysis. We present a strict guide how to speedup manual annotation process by 30 times and illustrate it with the sample test video sequences that consists of more than 100000 frames, 950 individuals and 75000 facial images. Experimental evaluation of the face detection using the ground truth data, that was semi-automatically labeled, demonstrates effectiveness of current approach both for learning and test stages.


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