Recognizability assessment of facial images for automated teller machine applications

Suhr, Jae Kyu; Eum, Sungmin; Jung, Ho Gi; Li, Gen; Kim, Gahyun; Kim, Jaihie

doi:10.1016/j.patcog.2011.11.014

Cited by 15 publications

(8 citation statements)

References 35 publications

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“…Suhr et al started with the rising crime rate of automated bank teller machines. As the machine's sensing system could not accurately determine sabotage, the offenders' facial expressions captured by the camera were identified, especially during the sabotage period, to build a model of facial expressions of emotion, and finally a correlation between facial expressions and insecure behaviour was identified [ 20 ]. Pantano collected data on the facial expressions of a large group of consumers when they were engaged in consumer behaviour to build a machine learning model and employed facial expression recognition system to evaluate consumer behaviour and activities, allowing salespeople to guide consumers according to their emotional activities [ 21 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Research on the Training and Management of Industrializing Workers in Prefabricated Building with Machine Vision and Human Behaviour Modelling Based on Industry 4.0 Era

Wang

Song

Yuan³

et al. 2022

Computational Intelligence and Neuroscience

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As countries around the world pay more and more attention to the sustainable development of the construction industry, the prefabricated building model has become the best construction type to achieve energy conservation and emission reduction. However, the prefabricated building entails higher technical requirements, and the workers involved in the construction must be trained to reduce the risks. For China, where the demographic dividend is gradually disappearing, how to quickly promote the industrializing workers process has become an urgent issue. This research focuses on the training and management of industrializing workers in prefabricated building. First, the facial images of the participants were collected from the actual test data, and the changes of participants’ facial expressions were analyzed through multitask convolutional neural network-Lighten Facial Expression Recognition (MTCNN-LFER). The results of the analysis were plugged into the facial expression recognition and evaluation model for industrializing workers training in this research to calculate the weights, and then all the weights were clustered through the improved SWEM-SAM method. The results show the following: (1) the values of objective data were used to judge the participating workers’ mastery of each knowledge and to evaluate whether they are qualified. (2) The evaluation results were used to analyze the risk events that may be caused by participating workers.

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Section: Literature Reviewmentioning

confidence: 99%

Research on the Training and Management of Industrializing Workers in Prefabricated Building with Machine Vision and Human Behaviour Modelling Based on Industry 4.0 Era

Wang

Song

Yuan³

et al. 2022

Computational Intelligence and Neuroscience

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show abstract

“…Kose and Dugelay [9] used a 2D+3D face mask attack database which was prepared for TABULA RASA research project. Suhr et al [10] have proposed a system which assesses the recognizability of facial images of ATM users to determine whether their faces are severely occluded using component-based face candidate generation and verification approach to handle various facial postures and acceptable partial occlusions. Matta et al [11] proposed an architecture that analyzes the texture of the facial images using multi-scale local binary patterns thereby providing a unique feature space for coupling spoofing detection and face recognition.…”

Section: IImentioning

confidence: 99%

Robust Framework for Antispoofing Face Recognition Mechanism for Mitigating Image Attacks

Solanki¹

2018

IJRASET

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Abstract:The challenge here is that the appearance of human face can change drastically due to various illumination conditions and there are also many camera-related factors that may influence the quality of images, which makes it hard to differentiate images from a live person from those from photos. Due to these, simply asking "what's in the image (e.g., human skin)" tends to be unreliable. Another strategy is to use various image processing techniques to extract features that highlight the difference between images from live human faces and those from photographs. Work on fraud detection capabilities for face is still limited and a substantial part of it is based on the flatness of the captured surface in front of the sensor during an attack. This is also true for approaches that examine the 3D nature of the face by employing additional devices, which is much more realistic now with the introduction of affordable consumer depth cameras. With the help of the advancements in 3D manufacturing technologies, easily attainable facial masks take the spoofing attacks one step further and introduce new challenges for counter measure studies. The lack of protection against biometric spoofing attacks is not exclusive to face biometrics [2]. While it is possible to spoof a face authentication system using make-up, plastic surgery or forged masks; photographs and videos are probably the most common threats. Moreover, due to the increasing popularity of social network websites, a great deal of multimedia content, specially videos and photographs, is available on the web that can be used to spoof a face authentication system. To mitigate vulnerabilities, effective countermeasures against face spoofing must to be deployed.

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“…In addition, component-based methods [10], [11] use facial component regions to detect face occlusion. Suhr et al proposed a face occlusion detection system, which combine the eye-mouth combinations and geometric constraints [10].…”

Section: Related Workmentioning

confidence: 99%

“…Suhr et al proposed a face occlusion detection system, which combine the eye-mouth combinations and geometric constraints [10]. Eum et al presented a face recognizability evaluation to use facial components and the exceptional occlusion handling (EOH) [11].…”

Section: Related Workmentioning

confidence: 99%

Face Occlusion Detection Using Skin Color Ratio and LBP Features for Intelligent Video Surveillance Systems

Kim

Yang

2016

Annals of Computer Science and Information Systems

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Abstract-A face occlusion detection scheme which is based on both skin color ratio (SCR) and Local Binary Pattern (LBP) feature, is proposed. The proposed method mainly consists of four steps: foreground extraction, head detection, feature extraction, and occlusion detection. First, foreground is extracted by codebook background subtraction algorithm. Then, the head region is located using HOG head detector. After that, the skin-color ratio and LBP feature are extracted. Finally, SVM is trained based on LBP feature. The recognition result of SVM and the result of skin-color ratio feature are merged by weighted voting strategy, and then occluded faces are classified as three categories: concealed, partially concealed, and visible. Experimental results show that the proposed detection system can achieve desirable results in intelligent video surveillance systems.

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Recognizability assessment of facial images for automated teller machine applications

Cited by 15 publications

References 35 publications

Research on the Training and Management of Industrializing Workers in Prefabricated Building with Machine Vision and Human Behaviour Modelling Based on Industry 4.0 Era

Research on the Training and Management of Industrializing Workers in Prefabricated Building with Machine Vision and Human Behaviour Modelling Based on Industry 4.0 Era

Robust Framework for Antispoofing Face Recognition Mechanism for Mitigating Image Attacks

Face Occlusion Detection Using Skin Color Ratio and LBP Features for Intelligent Video Surveillance Systems

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