Using deep autoencoders to learn robust domain-invariant representations for still-to-video face recognition
Video-based face recognition (FR) is a challenging task in real-world applications. In still-to-video FR, probe facial regions of interest (ROIs) are typically captured with lower-quality video cameras under unconstrained conditions, where facial appearances vary according to pose, illumination, sca...
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| Vydáno v: | 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) s. 1 - 6 |
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IEEE
01.08.2017
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| Abstract | Video-based face recognition (FR) is a challenging task in real-world applications. In still-to-video FR, probe facial regions of interest (ROIs) are typically captured with lower-quality video cameras under unconstrained conditions, where facial appearances vary according to pose, illumination, scale, expression, etc. These video ROIs are typically compared against facial models designed with high-quality reference still ROI of each target individual enrolled to the system. In this paper, an efficient Canonical Face Representation CNN (CFR-CNN) is proposed for accurate still-to-video FR from a single sample per person, where still and video ROIs are captured in different conditions. Given a facial ROI captured under unconstrained video conditions, the CRF-CNN reconstructs it as a high-quality canonical ROI for matching that corresponds to the conditons of reference still ROIs (e.g., well-illuminated, sharp, frontal views with neutral expression). A deep autoencoder network is trained using a novel weighted loss function that can robustly generate similar face embeddings for the same subjects. Then, during operations, those face embeddings belonging to pairs of still and video ROIs from a target individual are accurately matched using a fully-connected classification network. Experimental results obtained with the COX Face and Chokepoint datasets indicate that the proposed CFR-CNN can achieve convincing level of accuracy. The computational complexity (number of operations, network parameters and layers) is significantly lower than state-of-the-art CNNs for video FR, and suggests that the CFR-CNN represents a cost-effective solution for real-time applications. |
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| AbstractList | Video-based face recognition (FR) is a challenging task in real-world applications. In still-to-video FR, probe facial regions of interest (ROIs) are typically captured with lower-quality video cameras under unconstrained conditions, where facial appearances vary according to pose, illumination, scale, expression, etc. These video ROIs are typically compared against facial models designed with high-quality reference still ROI of each target individual enrolled to the system. In this paper, an efficient Canonical Face Representation CNN (CFR-CNN) is proposed for accurate still-to-video FR from a single sample per person, where still and video ROIs are captured in different conditions. Given a facial ROI captured under unconstrained video conditions, the CRF-CNN reconstructs it as a high-quality canonical ROI for matching that corresponds to the conditons of reference still ROIs (e.g., well-illuminated, sharp, frontal views with neutral expression). A deep autoencoder network is trained using a novel weighted loss function that can robustly generate similar face embeddings for the same subjects. Then, during operations, those face embeddings belonging to pairs of still and video ROIs from a target individual are accurately matched using a fully-connected classification network. Experimental results obtained with the COX Face and Chokepoint datasets indicate that the proposed CFR-CNN can achieve convincing level of accuracy. The computational complexity (number of operations, network parameters and layers) is significantly lower than state-of-the-art CNNs for video FR, and suggests that the CFR-CNN represents a cost-effective solution for real-time applications. |
| Author | Granger, Eric Parchami, Mostafa Bashbaghi, Saman Sayed, Saif |
| Author_xml | – sequence: 1 givenname: Mostafa surname: Parchami fullname: Parchami, Mostafa email: mostafa.parchami@mavs.uta.edu organization: Comput. Sci. & Eng. Dept., Univ. of Texas at Arlington, Arlington, TX, USA – sequence: 2 givenname: Saman surname: Bashbaghi fullname: Bashbaghi, Saman email: bashbaghi@livia.etsmtl.ca organization: Ecole de Technol. Super., Univ. du Quebec, Montreal, QC, Canada – sequence: 3 givenname: Eric surname: Granger fullname: Granger, Eric email: eric.granger@etsmtl.ca organization: Ecole de Technol. Super., Univ. du Quebec, Montreal, QC, Canada – sequence: 4 givenname: Saif surname: Sayed fullname: Sayed, Saif email: saif.sayed@uta.edu organization: Comput. Sci. & Eng. Dept., Univ. of Texas at Arlington, Arlington, TX, USA |
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| Snippet | Video-based face recognition (FR) is a challenging task in real-world applications. In still-to-video FR, probe facial regions of interest (ROIs) are typically... |
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| Title | Using deep autoencoders to learn robust domain-invariant representations for still-to-video face recognition |
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