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Attribute-Enhanced Face Recognition With Neural Tensor Fusion Networks

Deep learning has achieved great success in face recognition, however deep-learned features still have limited invariance to strong intra-personal variations such as large pose. It is observed that some facial attributes (e.g. eyebrow thickness, gender) are invariant to such variations. We present the first work to systematically explore how the fusion of face recognition feature (FRF) and facial attribute feature (FAF) can enhance face recognition performance in various challenging scenarios. Despite this helpfulness of FAF, in practice, we find the existing fusion methods cannot reliably improve the recognition performance. Thus, we develop a powerful tensor-based framework which formulates this fusion as a low-rank tensor optimisation problem. It is non-trivial to directly optimise this tensor due to the large number of parameters to optimise. To solve this problem, we establish a theoretical equivalence between tensor optimisation and a two-stream gated neural network. This equivalence allows tractable computation and the use of standard neural network optimisation tools, leading to an accurate and stable optimisation. Experimental results show the fused feature works better than individual features thus proving for the first time that facial attributes aid face recognition. We achieve state-of-the-art performance on databases such as MultiPIE, CASIA NIR-VIR2.0 and LFW.

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