Fine-grained face verification: FGLFW database, baselines, and human-DCMN partnership
As performance on some aspects of the Labeled Faces in the Wild (LFW) benchmark approaches 100% accuracy, there is an intense debate on whether unconstrained face verification problem has already been solved. In this paper, we study a new face verification problem that assumes the imposter would del...
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| Published in: | Pattern recognition 2017-06, Vol.66, p.63-73 |
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| Main Authors: | , , , , |
| Format: | Article |
| Language: | eng |
| Subjects: | |
| Online Access: | Get full text |
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| Summary: | As performance on some aspects of the Labeled Faces in the Wild (LFW) benchmark approaches 100% accuracy, there is an intense debate on whether unconstrained face verification problem has already been solved. In this paper, we study a new face verification problem that assumes the imposter would deliberately seek a people with similarly-looking face to invade the biometric system. To simulate this deliberate imposture attack, we first construct a Fine-Grained LFW (FGLFW) database, which deliberately selects 3000 similarly-looking face pairs within original image folders by human crowdsourcing to replace the negative pairs of LFW. Our controlled human survey reports 99.85% accuracy on LFW, but only 92.03% accuracy on FGLFW. As the algorithm baselines, we evaluate several state-of-the-art metric learning, face descriptors, and deep learning methods on the new FGLFW database, and their accuracy drops about 10–20% compared to the corresponding LFW performance. To address this challenge, we develop a Deep Convolutional Maxout Network (DCMN) which aim to tolerate the multi-modal intra-personal variations and distinguish fine-grained localized inter-personal facial details. The experimental results suggest that the proposed DCMN method significantly outperforms current techniques such as Deepface, DeepID2, and VGG-Face. Fusion of the scores of our proposed DCMN to that of human operators notably boost the verification accuracy from 92–96%, suggesting that human-algorithm partnerships are promising to detect the similarly-looking deliberate impostors.
•New fine-grained face verification task to detect the imposter who deliberately seek a people with similarly-looking face.•Fine-Grained LFW (FGLFW) database that evaluates the new fine-grained face verification task.•Controlled human survey reports 99.85% accuracy on LFW, but only 92.03% accuracy on FGLFW.•Deep Convolutional Maxout Network (DCMN) that outperforms current techniques such as Deepface, DeepID2, and VGG-Face on LFW and FGLFW experiments.•The finding that human and DCMN are highly complementary on the fine-grained face verification task. |
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| ISSN: | 0031-3203 1873-5142 |