Face recognition: Past, present and future (a review)

Taşkıran, Murat; Kahraman, Nihan; Erdem, Çiğdem Eroğlu

doi:10.1016/j.dsp.2020.102809

Cited by 158 publications

(72 citation statements)

References 352 publications

(429 reference statements)

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“…There are also recognition errors for the object being tested. [3], the study yielded a higher RR of 99.67%. The proposed method which used the combination of the median filter and Haar cascade classifier also obtained a higher RR than the co-occurrence matrix of LMBP features (COM + LMBP) with 96.25% RR [14], the LBP + PCA + LDA with 98% RR [21] and CoLBP features with 97.28% RR [7].…”

mentioning

confidence: 69%

“…Taskiran, Kahraman, and Erdem recently reported that facial biometric data is one of the most preferred types of biometric data as it generally does not require the cooperation of the user and can be obtained without violating private space [3]. Wei, Chen, Li, Jiang, Zhao, and Li proposed an adaptive dictionary-learning method based on local configuration patterns for face recognition.…”

Section: Literature Reviewmentioning

confidence: 99%

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Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images

Isnanto

Rochim

Eridani

et al. 2021

Int. j. eng. technol. innov.

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This study aims to build a face recognition prototype that can recognize multiple face objects within one frame. The proposed method uses a local binary pattern histogram and Haar cascade classifier on low-resolution images. The lowest data resolution used in this study was 76 × 76 pixels and the highest was 156 × 156 pixels. The face images were preprocessed using the histogram equalization and median filtering. The face recognition prototype proposed successfully recognized four face objects in one frame. The results obtained were comparable for local and real-time stream video data for testing. The RR obtained with the local data test was 99.67%, which indicates better performance in recognizing 75 frames for each object, compared to the 92.67% RR for the real-time data stream. In comparison to the results obtained in previous works, it can be concluded that the proposed method yields the highest RR of 99.67%.

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mentioning

confidence: 69%

Section: Literature Reviewmentioning

confidence: 99%

Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images

Isnanto

Rochim

Eridani

et al. 2021

Int. j. eng. technol. innov.

View full text Add to dashboard Cite

show abstract

“…Taskiran et al [12] present a taxonomy for face recognition as an image-based and video-based method. For video-based face recognition, various recent deep learning methods were discussed by grouping as set-based method and sequence-based method.…”

Section: A Literature Surveymentioning

confidence: 99%

Video Processing Using Deep Learning Techniques: A Systematic Literature Review

et al. 2021

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Studies show lots of advanced research on various data types such as image, speech, and text using deep learning techniques, but nowadays, research on video processing is also an emerging field of computer vision. Several surveys are present on video processing using computer vision deep learning techniques, targeting specific functionality such as anomaly detection, crowd analysis, activity monitoring, etc. However, a combined study is still unexplored. This paper aims to present a Systematic Literature Review (SLR) on video processing using deep learning to investigate the applications, functionalities, techniques, datasets, issues, and challenges by formulating the relevant research questions (RQs). This systematic mapping includes 93 research articles from reputed databases published between 2011 and 2020. We categorize the deep learning technique for video processing as CNN, DNN, and RNN based. We observe the significant advancements in video processing between 2017 and 2020, primarily due to the advent of AlexNet, ResNet, and LSTM based deep learning techniques. The prominent fields of video processing research are observed as human action recognition, crowd anomaly detection, and behavior analysis. This SLR is a helpful guide for the researchers to explore the recent literature, available datasets, and existing deep learning techniques for video processing.

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“…The human face is used as a biometric trait in various areas, such as human-computer interaction, health, education, security and law enforcement, entertainment, marketing, and finance [229]. Face recognition technologies can discreetly extract information from an individual's face, such as emotions, mental states, race, identity, age, and gender [230,231].…”

Section: Face Recognitionmentioning

confidence: 99%

Application of Eye Tracking Technology in Aviation, Maritime, and Construction Industries: A Systematic Review

Martinez-Marquez

Pingali

Panuwatwanich

et al. 2021

Sensors

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Most accidents in the aviation, maritime, and construction industries are caused by human error, which can be traced back to impaired mental performance and attention failure. In 1596, Du Laurens, a French anatomist and medical scientist, said that the eyes are the windows of the mind. Eye tracking research dates back almost 150 years and it has been widely used in different fields for several purposes. Overall, eye tracking technologies provide the means to capture in real time a variety of eye movements that reflect different human cognitive, emotional, and physiological states, which can be used to gain a wider understanding of the human mind in different scenarios. This systematic literature review explored the different applications of eye tracking research in three high-risk industries, namely aviation, maritime, and construction. The results of this research uncovered the demographic distribution and applications of eye tracking research, as well as the different technologies that have been integrated to study the visual, cognitive, and attentional aspects of human mental performance. Moreover, different research gaps and potential future research directions were highlighted in relation to the usage of additional technologies to support, validate, and enhance eye tracking research to better understand human mental performance.

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Face recognition: Past, present and future (a review)

Cited by 158 publications

References 352 publications

Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images

Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images

Video Processing Using Deep Learning Techniques: A Systematic Literature Review

Application of Eye Tracking Technology in Aviation, Maritime, and Construction Industries: A Systematic Review

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