Comparison of Computer and Human Face Recognition According to Facial Components

Nam, Hyun-Ha,Kang, Byung-Jun,Park, Kang-Ryoung Korea Multimedia Society 2012 멀티미디어학회논문지 Vol.15 No.1

Face recognition is a biometric technology used to identify individuals based on facial feature information. Previous studies of face recognition used features including the eye, mouth and nose; however, there have been few studies on the effects of using other facial components, such as the eyebrows and chin, on recognition performance. We measured the recognition accuracy affected by these facial components, and compared the differences between computer-based and human-based facial recognition methods. This research is novel in the following four ways compared to previous works. First, we measured the effect of components such as the eyebrows and chin. And the accuracy of computer-based face recognition was compared to human-based face recognition according to facial components. Second, for computer-based recognition, facial components were automatically detected using the Adaboost algorithm and active appearance model (AAM), and user authentication was achieved with the face recognition algorithm based on principal component analysis (PCA). Third, we experimentally proved that the number of facial features (when including eyebrows, eye, nose, mouth, and chin) had a greater impact on the accuracy of human-based face recognition, but consistent inclusion of some feature such as chin area had more influence on the accuracy of computer-based face recognition because a computer uses the pixel values of facial images in classifying faces. Fourth, we experimentally proved that the eyebrow feature enhanced the accuracy of computer-based face recognition. However, the problem of occlusion by hair should be solved in order to use the eyebrow feature for face recognition.

A Survey of Unconstrained Face Recognition Algorithm and Its Applications

Xianyou Zhu 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.12

Face-recognition is becoming common among the section of study in computer-vision, while it is also one of the very effective programs of comprehension and image-analysis. It may be employed for both ID and confirmation. At the moment, there are lots of means of front watch face-recognition. Nicely when just one instant picture per course can be obtained nevertheless, a handful of them can perhaps work. In this paper, we discuss the different face recognition techniques and find a better method for pose variation, non-uniform motion blur and Illumination by using a Reference face graph for face recognition. One example image' problem and two generalized eigenface algorithms are proposed. Face-recognition has been analyzed thoroughly; nevertheless, real world face-recognition stays a job that is difficult. The interest in unconstrained useful face-recognition is increasing using the surge of online media, for example, video-surveillance video, and internet sites wherever encounter evaluation is of substantial significance. Face-recognition is approached by us within data theory's framework. We identify an unfamiliar encounter utilizing an exterior Reference Face Graph (RFG). There is an RFG produced by evaluating it towards the encounters within the built RFG and acknowledgement of the given encounter is attained. Centrality steps are used to recognize encounters that were unique within the Reference Face Graph.

A Novel Face Recognition Algorithm Based on Improved Retinex and Sparse Representation

Jingyi bo,Yubin Wang,Zhuo Lin 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.8

In recent years, face recognition technology has been widely used as a kind of important modern biological recognition technology. As one of the main factors that affect the recognition rate, the illumination variation has attracted the attention of many researchers. In order to improve the face recognition under illumination variation condition, a novel face recognition algorithm based on improved Retinex and sparse representation is proposed in this paper. Retinex algorithm can be used to solve the problem of face illumination variation in face recognition, but it is easy to produce ‘halo’ phenomenon. In order to improve the face recognition rate under the change of illumination condition. In this paper, firstly, in order to eliminate the interference of illumination on face recognition, we apply the Retinex that is improved by partial differential equations to face image processing. Then, sparse representation is used to extract face feature vector, and the voting method is used to realize the face recognition. Finally, the performance of the algorithm is tested by 3 standard face databases. The results show that the proposed algorithm can improve the face recognition rate under different illumination conditions, and has good robustness to illumination.

Efficient Head Pose Determination and Its Application to Face Recognition on Multi-Pose Face DB

Jun Lee,Jeong-Sik Park,Gil-Jin Jang,Yong-Ho Seo 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.2

Face recognition is a well-known approach for identity recognition. Variation in head pose is a main factor that interferes with face recognition systems. This paper proposes an efficient head pose determination method and its application to face recognition on a multi-pose face DB in order to solve the pose variation-related problem. The first step is to detect a facial region using Adaboost. Next, after undergoing preprocessing on the detected face, a mask is placed to cover it. At the detected facial region, the pose is determined by relations of the position of the centroid points of the eyes and lip regions detected by using ellipse-fitting method. Finally, face recognition is conducted by applying template matching between a set of facial images in multi-pose face DB pertinent to the determined head pose and the input face image. In experiments, the proposed approach outperformed the conventional PCA-based face recognition approach depending on a single-pose face DB.

Face Recognition Using Neural Network : A Review

Manisha M. Kasar,Debnath Bhattacharyya,Tai-hoon Kim 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.3

Face recognition from the real data, capture images, sensor images and database images is challenging problem due to the wide variation of face appearances, illumination effect and the complexity of the image background. Face recognition is one of the most effective and relevant applications of image processing and biometric systems. In this paper we are discussing the face recognition methods, algorithms proposed by many researchers using artificial neural networks (ANN) which have been used in the field of image processing and pattern recognition. How ANN will used for the face recognition system and how it is effective than another methods will also discuss in this paper. There are many ANN proposed methods which give overview face recognition using ANN. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included, and also the performance analysis of different ANN approach and algorithm is analysing in this research study.

A Study on the Face Recognition based on the Face Geometrical Characteristics

Dadong Zhao,Jeong-Young Song 한국정보기술학회 2010 Proceedings of KIIT Conference Vol.2010 No.-

This paper has analyzed face characteristics and designed the face features, then it extracts the features and constructed the feature values, and at last, it realizes face recognition through matching of similarity of the feature values. According to the obvious difference of the five sense organs in color, using rgb color space to analyze the distribution range of the five sense organs, and in accordance with the statistical results, the five sense organs in the face are segmented and geometrical parameters of the relevant facial parts are obtained. Construction face feature values with the feature values formed with the proportion of these parameters. Face recognition is to match the feature values. As the extracted feature values have different stability and contribution to the recognition, this paper grants different coefficients to the components, and then uses weighted similarity calculation method to work out the similarity value. Finally, this paper set the maximum similarity matching as the basis for face recognition. For the selection of feature components, we have comprehensively considered the overall features and partial features of face and combined the face shape features and the features of the five sense organs. The experiment shows that these features can reflect the individual features of human face, which can be treated as an effective basis for face recognition, thus to realize face recognition.

Robust face recognition via hierarchical collaborative representation

Vo, Duc My,Lee, Sang-Woong Elsevier science 2018 Information sciences Vol.432 No.-

<P><B>Abstract</B></P> <P>Collaborative representation-based classification (CRC) is currently attracting the attention of researchers because it is more effective than conventional representation-based classifiers in recognition tasks. CRC has shown high face recognition accuracy; however, its accuracy is degraded significantly if the number of training faces in each class is small. This is because the accuracy of CRC is only dependent on the results of minimizing the Euclidean distance between a testing face and its approximator in the collaborative subspace of training faces. In this research, we proved that the accuracy of CRC can be improved substantially by minimizing not only the Euclidean distance between a testing face and its approximator but also the Euclidean distances from the approximator to training faces in each class. Consequently, we presented a hierarchical collaborative representation-based classification (HCRC) in which a two-stage classifier is applied for training faces, and the recognition accuracy of the second-stage classifier is significantly improved in comparison to that of the first-stage classifier. Moreover, the recognition rate of our classifier can be considerably increased by using models of discriminative feature extraction. Since noise and illumination are the main factors that cause CRC to be less accurate, we propose combining HCRC with a wide model of local ternary patterns (LTP). This combination enhances the efficiency of face recognition under different illumination and noisy conditions. For dealing with face recognition under variations in pose, expression and illumination, we present a deep convolutional neural network (DCNN) model of discriminative feature learning, which transforms face images into a common set of distinct features. The combination of HCRC with this deep model achieves high recognition rates on challenging face databases. Furthermore both models are optimized to reduce computational costs so that they can be successfully applied for real-world applications of face recognition that are required to run reliably in real time. In addition, we also prove that combining state-of-the-art DCNN models with HCRC results in an significant improvement in face recognition performance. We demonstrate several experiments with challenging face recognition datasets. Our results show that the hierarchical collaborative representation-based classifier with the models significantly outperforms state-of-the-art methods.</P>

다면기법 SPFACS 영상객체를 이용한 AAM 알고리즘 적용 미소검출 설계 분석

최병관,Choi, Byungkwan 디지털산업정보학회 2015 디지털산업정보학회논문지 Vol.11 No.3

Digital imaging technology has advanced beyond the limits of the multimedia industry IT convergence, and to develop a complex industry, particularly in the field of object recognition, face smart-phones associated with various Application technology are being actively researched. Recently, face recognition technology is evolving into an intelligent object recognition through image recognition technology, detection technology, the detection object recognition through image recognition processing techniques applied technology is applied to the IP camera through the 3D image object recognition technology Face Recognition been actively studied. In this paper, we first look at the essential human factor, technical factors and trends about the technology of the human object recognition based SPFACS(Smile Progress Facial Action Coding System)study measures the smile detection technology recognizes multi-faceted object recognition. Study Method: 1)Human cognitive skills necessary to analyze the 3D object imaging system was designed. 2)3D object recognition, face detection parameter identification and optimal measurement method using the AAM algorithm inside the proposals and 3)Face recognition objects (Face recognition Technology) to apply the result to the recognition of the person's teeth area detecting expression recognition demonstrated by the effect of extracting the feature points.

A Study on the Recognition of Face Based on CNN Algorithms

Da-Yeon Son(손다연),Kwang-Keun Lee(이광근) 한국BIM학회 2017 KIBIM Magazine Vol.5 No.2

Recently, technologies are being developed to recognize and authenticate users using bioinformatics to solve information security issues. Biometric information includes face, fingerprint, iris, voice, and vein. Among them, face recognition technology occupies a large part. Face recognition technology is applied in various fields. For example, it can be used for identity verification, such as a personal identification card, passport, credit card, security system, and personnel data. In addition, it can be used for security, including crime suspect search, unsafe zone monitoring, vehicle tracking crime.In this thesis, we conducted a study to recognize faces by detecting the areas of the face through a computer webcam. The purpose of this study was to contribute to the improvement in the accuracy of Recognition of Face Based on CNN Algorithms. For this purpose, We used data files provided by github to build a face recognition model. We also created data using CNN algorithms, which are widely used for image recognition. Various photos were learned by CNN algorithm. The study found that the accuracy of face recognition based on CNN algorithms was 77%. Based on the results of the study, We carried out recognition of the face according to the distance. Research findings may be useful if face recognition is required in a variety of situations. Research based on this study is also expected to improve the accuracy of face recognition.

A Study on the Recognition of Face Based on CNN Algorithms

Da-Yeon Son,이광근 한국인공지능학회 2017 인공지능연구 (KJAI) Vol.5 No.2

Recently, technologies are being developed to recognize and authenticate users using bioinformatics to solve information security issues. Biometric information includes face, fingerprint, iris, voice, and vein. Among them, face recognition technology occupies a large part. Face recognition technology is applied in various fields. For example, it can be used for identity verification, such as a personal identification card, passport, credit card, security system, and personnel data. In addition, it can be used for security, including crime suspect search, unsafe zone monitoring, vehicle tr acking crime.In this thesis, we conducted a study to recognize faces by detecting the areas of the face thr ough a computer webcam. The purpose of this study was to contribute to the improvement in the accuracy of Recognition of Face Based on CNN Algorithms. For this purpose, We used data files provided by github to build a face recognition model. We also created data using CNN algorithms, which are widely used for image recognition. Various photos were learned by CNN algorithm. The study found that the accuracy of face recognition based on CNN algorithms was 77%. Based on the results of the study, We carried out recognition of the face according to the distance. Research findings may be useful if face recognition is required in a variety of situations. Research based on this study is also expected to improve the accuracy of face recognition.