Novel approach to enhance face recognition using depth maps

Saleh, Yaser; Edirisinghe, Eran A.

doi:10.1109/iwssip.2016.7502699

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…Recently, thermal image-based and depth-image-based face recognition systems have been proposed in addition to the visible-light face recognition systems [29,30]. The use of these special imaging devices can help reduce the negative effects of illumination on face recognition systems.…”

Section: Related Workmentioning

confidence: 99%

Visible-Light Camera Sensor-Based Presentation Attack Detection for Face Recognition by Combining Spatial and Temporal Information

Nguyen

Pham

Lee

et al. 2019

Sensors

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Face-based biometric recognition systems that can recognize human faces are widely employed in places such as airports, immigration offices, and companies, and applications such as mobile phones. However, the security of this recognition method can be compromised by attackers (unauthorized persons), who might bypass the recognition system using artificial facial images. In addition, most previous studies on face presentation attack detection have only utilized spatial information. To address this problem, we propose a visible-light camera sensor-based presentation attack detection that is based on both spatial and temporal information, using the deep features extracted by a stacked convolutional neural network (CNN)-recurrent neural network (RNN) along with handcrafted features. Through experiments using two public datasets, we demonstrate that the temporal information is sufficient for detecting attacks using face images. In addition, it is established that the handcrafted image features efficiently enhance the detection performance of deep features, and the proposed method outperforms previous methods.

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Section: Related Workmentioning

confidence: 99%