pith. sign in

arxiv: 1702.04280 · v2 · pith:6XQFMTFTnew · submitted 2017-02-14 · 💻 cs.CV · cs.AI

DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network

classification 💻 cs.CV cs.AI
keywords deepemotiongenderrecognitionseveralsighthoundsystembenchmarks
0
0 comments X
read the original abstract

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

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. AESOP: Adversarial Execution-path Selection to Overload Deep Learning Pipelines

    cs.LG 2026-05 unverdicted novelty 8.0

    AESOP enables path-aware adversarial attacks that inflate FLOPs in ML pipelines by up to 2407x, 20x more than single-model baselines, even under defenses that force throughput collapse or data loss.