DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network
By Afshin Dehghan, Enrique G. Ortiz, Guang Shu, Syed Zain Masood. 2017
104 citations recorded by the source corpus.
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 this https URL
Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology
How researchers see this paper
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In Computer Vision and Pattern Recognition
- Wojciech Samek
- K. Müller
- Sebastian Lapuschkin
- Grgoire Montavon
- Alexander Binder
- Joy Buolamwini
- Timnit Gebru
- Christopher J. Anders
Citations in the corpus
- Understanding and Comparing Deep Neural Networks for Age and Gender Classification (2017, methodology): “To the best of our knowledge, the current state of the art results for age and gender predictions are reported in [32] and [7] with 64% and 91% accuracy respectively.”
- Mitigating Bias in Gender, Age and Ethnicity Classification: A Multi-task Convolution Neural Network Approach (2018, background): “Moreover nowadays, FA is not restricted to face recognition, but has transgressed onto emotion analysis [5], gender classification [6], as well as other facial characteristics [7–9].”
- Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018, background): “For example, companies such as Af-fectiva (Affectiva) and researchers in academia attempt to identify emotions from images of people’s faces (Dehghan et al., 2017; Srinivasan et al., 2016; Fabian Benitez-Quiroz et al., 2016).”