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Virtual applications through mobile platforms are one of the most criticaland ever-growing fields in AI, where ubiquitous and real-time personauthentication has become critical after the breakthrough of all servicesprovided via mobile devices. In this context, face verification technologiescan provide reliable and robust user authentication, given the availability ofcameras in these devices, as well as their widespread use in everydayapplications. The rapid development of deep Convolutional Neural Networks hasresulted in many accurate face verification architectures. However, theirtypical size (hundreds of megabytes) makes them infeasible to be incorporatedin downloadable mobile applications where the entire file typically may notexceed 100 Mb. Accordingly, we address the challenge of developing alightweight face recognition network of just a few megabytes that can operatewith sufficient accuracy in comparison to much larger models. The network alsoshould be able to operate under different poses, given the variabilitynaturally observed in uncontrolled environments where mobile devices aretypically used. In this paper, we adapt the lightweight SqueezeNet model, ofjust 4.4MB, to effectively provide cross-pose face recognition. After trainedon the MS-Celeb-1M and VGGFace2 databases, our model achieves an EER of 1.23%on the difficult frontal vs. profile comparison, and0.54% on profile vs.profile images. Under less extreme variations involving frontal images in anyof the enrolment/query images pair, EER is pushed down to<0.3%, and the FRR atFAR=0.1%to less than 1%. This makes our light model suitable for facerecognition where at least acquisition of the enrolment image can becontrolled. At the cost of a slight degradation in performance, we also test aneven lighter model (of just 2.5MB) where regular convolutions are replaced withdepth-wise separable convolutions.

SqueezeFacePoseNet: Lightweight Face Verification Across Different Poses for Mobile Platforms