Face Recognition: A Literature Review

Barnouti, Nawaf Hazim; Al-Dabbagh, Sinan Sameer Mahmood; Matti, Wael Esam

doi:10.5120/ijais2016451597

Cited by 22 publications

(18 citation statements)

References 15 publications

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“…Face recognition is one of the most efficient biometric techniques to identify a person [11], and has advantages compared to other biometric methods, such as identifying could be done without requiring action from the user, it has non-intrusive characteristics [12]. Face recognition is a technology that can be applied in various fields, such as surveillance, smart cards, entertainment, law enforcement, information security, image database investigation, civilian applications, human-computer interactions [13].…”

Section: B Face Recognitionmentioning

confidence: 99%

“…Face recognition used digital image or video as an input and data of the person that appears in the image or video processed as an output [13]. The face recognition process can be divided into two parts, the first part is image processing, which consists of obtaining facial images through scanning, image quality improvement, image cropping, image filtering, edge detection, and extracting features in images.…”

Section: B Face Recognitionmentioning

confidence: 99%

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Automatic Attendance System for University Student Using Face Recognition Based on Deep Learning

Sutabri¹,

Pamungkur²,

Kurniawan³

et al. 2019

IJMLC

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Student attendance is essential in the learning process. To record student attendance, several ways can be done; one of them is through student signatures. The process has several shortcomings, such as requiring a long time to make attendance; the attendance paper is lost, the administration must enter attendance data one by one into the computer. To overcome this, the paper proposed a web-based student attendance system that uses face recognition. In the proposed system, Convolutional Neural Network (CNN) is used to detect faces in images, deep metric learning is used to produce facial embedding, and K-NN is used to classify student's faces. Thus, the computer can recognize faces. From the experiments conducted, the system was able to recognize the faces of students who did attend and their attendance data was automatically saved. Thus, the university administration is alleviated in recording attendance data.

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Section: B Face Recognitionmentioning

confidence: 99%

Section: B Face Recognitionmentioning

confidence: 99%

Automatic Attendance System for University Student Using Face Recognition Based on Deep Learning

Sutabri¹,

Pamungkur²,

Kurniawan³

et al. 2019

IJMLC

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“…Then, Euclidean Distance is computed for all images in training with the test image. A minimum Euclidean Distance classifier is used as a condition in order to find the best-matched test image in the traini ng samples (Barnouti, Mahmood, & Matti, 2016)…”

Section: =0mentioning

confidence: 99%

“…3) Finally, finding the principal component of this set by computing the eigenvectors of the covariance matrix C, as in(4).C=∑ ′ ′ =1 (4) The training set faces are represented by the obtained eigenvectors which are used as an orthogonal basis in this process (Nicholl, Ahmad, & Amira, 2010). 3.2 Discrete wavelet transforms Transformation a signal is just another form of representing this signal.…”

mentioning

confidence: 99%

Discrete Wavelet Transform with Eigenface to Enhance Face Recognition Rate

2018

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In a recent past, face recognition was one of the most popular methods and successful application of image processing field which is widely used in security and biometric applications. The innovation of new approaches to face identification technologies is continuously subject to building much strong face recognition algorithms. Face recognition in real-time applications has been fast-growing challenging and interesting. The human face identification process is not trivial task especially different face lighting and poses are captured to be matched. In this study, the proposed method is tested using a benchmark ORL database that contains 400 images of 40 persons as the variant posse, lighting, etc. Discrete avelet Transform technique is applied on the ORL database to enhance the accuracy and the recognition rate. The best recognition rate result obtained is 99.25%, when tested using 9 training images and 1 testing image with cosine distance measurement. The recognition rate Increased when applying 2-level of DWT with the bior5.5 filter on training image database and the test image. For feature extraction and dimension reduction, PCA is used. Euclidean distance, Manhattan distance, and Cosine distance are Distance measures used for the matching process.

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“…Understanding the underlying features can help endow a variety of artificial-intelligence applications with humanlike performance. While face detection itself has been extensively studied in computer vision (Viola & Jones, 2001; Barnouti, Al-Dabbagh, & Matti, 2016; M. Wang & Deng, 2018), face categorization has largely been studied using only coarse distinctions such as ethnicity (Caucasian/Black/Asian; Brooks & Gwinn, 2010; Fu, He, & Hou, 2014) and gender (Tariq, Hu, & Huang, 2009; Fu, He & Hou, 2014; Y. Wang, Liao, Feng, Xu, & Luo, 2016). Even in humans, only coarse distinctions such as Caucasian/Black have been studied (Brooks & Gwinn, 2010; Fu, He & Hou, 2014).…”

Section: Introductionmentioning

confidence: 99%

Are you from North or South India? A hard face-classification task reveals systematic representational differences between humans and machines

Katti

Arun

2019

Journal of Vision

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We make a rich variety of judgments on faces, but the underlying features are poorly understood. Here we describe a challenging geographical-origin classification problem that elucidates feature representations in both humans and machine algorithms. In Experiment 1 , we collected a diverse set of 1,647 faces from India labeled with their fine-grained geographical origin (North vs. South India), characterized the categorization performance of 129 human subjects on these faces, and compared this with the performance of machine vision algorithms. Our main finding is that while many machine algorithms achieved an overall performance comparable to that of humans (64%), their error patterns across faces were qualitatively different despite training. To elucidate the face parts used by humans for classification, we trained linear classifiers on overcomplete sets of features derived from each face part. This revealed mouth shape to be the most discriminative part compared to eyes, nose, or external contour. In Experiment 2 , we confirmed that humans relied the most on mouth shape for classification using an additional experiment in which subjects classified faces with occluded parts. In Experiment 3 , we compared human performance for briefly viewed faces and for inverted faces. Interestingly, human performance on inverted faces was predicted better by computational models compared to upright faces, suggesting that humans use relatively more generic features on inverted faces. Taken together, our results show that studying hard classification tasks can lead to useful insights into both machine and human vision.

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Face Recognition: A Literature Review

Cited by 22 publications

References 15 publications

Automatic Attendance System for University Student Using Face Recognition Based on Deep Learning

Automatic Attendance System for University Student Using Face Recognition Based on Deep Learning

Discrete Wavelet Transform with Eigenface to Enhance Face Recognition Rate

Are you from North or South India? A hard face-classification task reveals systematic representational differences between humans and machines

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