Real-time acquisition of high quality face sequences from an active pan-tilt-zoom camera

Haque, Mohammad A.; Nasrollahi, Kamal; Moeslund, Thomas B.

doi:10.1109/avss.2013.6636680

Cited by 20 publications

(16 citation statements)

References 20 publications

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“…The first step is capturing the facial video using a RGB camera, which is thoroughly investigated in the literature [11,12]. As recent methods of facial video based heartbeat signal analysis utilized simple webcam for video capturing, we select a webcam based video capturing procedure.…”

Section: Facial Video Acquisitionmentioning

confidence: 99%

Can contact-free measurement of heartbeat signal be used in forensics?

Haque¹,

Nasrollah²,

Moeslund³

2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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Biometrics and soft biometrics characteristics are of great importance in forensics applications for identifying criminals and law enforcement. Developing new biometrics and soft biometrics are therefore of interest of many applications, among them forensics. Heartbeat signals have been previously used as biometrics, but they have been measured using contact-based sensors. This paper extracts heartbeat signals, using a contact-free method by a simple webcam. The extracted signals in this case are not as precise as those that can be extracted using contact-based sensors. However, the contact-free extracted heartbeat signals are shown in this paper to have some potentials to be used as soft biometrics. Promising experimental results on a public database, have shown that utilizing these signals can improve the accuracy of spoofing detection in a face recognition system.

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Section: Facial Video Acquisitionmentioning

confidence: 99%

Can contact-free measurement of heartbeat signal be used in forensics?

Haque¹,

Nasrollah²,

Moeslund³

2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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“…There are many researches about optimal face acquisition in a surveillance camera or a single sequence of images [1][2][3][4]. Fourney et al [1] proposed a high quality face image selecting method by integrating 6 quality criteria with linear weighting.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous feature fusion-based optimal face image acquisition in visual sensor network

Lin

Wang

Cui

et al. 2015

2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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High quality face image acquisition from huge video data obtained in visual sensor network is of great significance in applications related to face processing, such as face recognition and reconstruction. This paper proposes an optimal face image acquisition method in visual sensor network, which is based on collaborative face frames acquisition and heterogeneous feature fusion-based face quality assessment. Gaussian-probabilitydistribution-based multi-view data fusion and kalman filter are used for collaborative target localization and tracking. To achieve primary screening of face frames, a lightweight face frames quality evaluation method is presented. Importantly, new face quality assessment criterion calculation methods are proposed to make fine screening of face images more applicable in visual sensor network. The new face quality assessment criterion calculation methods are based on heterogeneous feature fusion of pedestrian tracking and static face image features analysis. Fuzzy inference engine is used to combine these criteria to generate a face quality assessment score. Experimental results show that the proposed method can acquire optimal face images accurately and robustly.

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“…low face resolution) can also play important role as studied in [13,12,11]. The first limitation of the image resolution is created by the imaging acquisition devices or the imaging sensors [42].…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal Pain Recognition in CNN-Based Super-Resolved Facial Images

Bellantonio

Haque

Rodríguez

et al. 2017

Video Analytics. Face and Facial Expression Recognition and Audience Measurement

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Automatic pain detection is a long expected solution to a prevalent medical problem of pain management. This is more relevant when the subject of pain is young children or patients with limited ability to communicate about their pain experience. Computer vision-based analysis of facial pain expression provides a way of efficient pain detection. When deep machine learning methods came into the scene, automatic pain detection exhibited even better performance. In this paper, we figured out three important factors to exploit in automatic pain detection: spatial information available regarding to pain in each of the facial video frames, temporal axis information regarding to pain expression pattern in a subject video sequence, and variation of face resolution. We employed a combination of convolutional neural network and recurrent neural network to setup a deep hybrid pain detection framework that is able to exploit both spatial and temporal pain information from facial video. In order to analyze the effect of different facial resolutions, we introduce a super-resolution algorithm to generate facial video frames with different resolution setups. We investigated the performance on the publicly available UNBC-McMaster Shoulder Pain database. As a contribution, the paper provides novel and important information regarding to the performance of a hybrid deep learning framework for pain detection in facial images of different resolution.

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Real-time acquisition of high quality face sequences from an active pan-tilt-zoom camera

Abstract: Abstract

Cited by 20 publications

References 20 publications

Can contact-free measurement of heartbeat signal be used in forensics?

Can contact-free measurement of heartbeat signal be used in forensics?

Heterogeneous feature fusion-based optimal face image acquisition in visual sensor network

Spatio-temporal Pain Recognition in CNN-Based Super-Resolved Facial Images

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