Face Attribute Detection with MobileNetV2 and NasNet-Mobile
Frerk Saxen, Philipp Werner, Sebastian Handrich, Ehsan Othman, Laslo Dinges, Ayoub K. Al-Hamadi · 2019
In this paper, we propose two simple yet effective methods to estimate facial attributes in unconstrained images. We use a straight forward and fast face alignment technique for preprocessing and estimate the face attributes using MobileNetV2 and Nasnet-Mobile, two lightweight CNN (Convolutional Neural Network) architectures. Both architectures perform similarly well in terms of accuracy and speed. A comparison with state-of-the-art methods with respect to processing time and accuracy shows that our proposed approach perform faster than the best state-of-the-art model and better than the fastest state-of-the-art model. Moreover, our approach is easy to use and capable of being deployed on mobile devices.