DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network
This work addresses the problem of efficient and accurate facial attribute recognition for developers, though it appears incremental as it builds on existing deep learning methods.
The paper tackled automated age, gender, and emotion recognition by developing a system using deep convolutional neural networks, achieving state-of-the-art results on competitive benchmarks with computationally inexpensive models.
This paper describes the details of Sighthound's fully automated age, gender and emotion recognition system. The backbone of our system consists of several deep convolutional neural networks that are not only computationally inexpensive, but also provide state-of-the-art results on several competitive benchmarks. To power our novel deep networks, we collected large labeled datasets through a semi-supervised pipeline to reduce the annotation effort/time. We tested our system on several public benchmarks and report outstanding results. Our age, gender and emotion recognition models are available to developers through the Sighthound Cloud API at https://www.sighthound.com/products/cloud