Towards a Real-Time Facial Analysis System
Adhikari, Bishwo; Ni, Xingyang; Rahtu, Esa; Huttunen, Heikki (2021)
Adhikari, Bishwo
Ni, Xingyang
Rahtu, Esa
Huttunen, Heikki
IEEE
2021
IEEE 23rd International Workshop on Multimedia Signal Processing, MMSP 2021
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202205034260
https://urn.fi/URN:NBN:fi:tuni-202205034260
Kuvaus
Peer reviewed
Tiivistelmä
Facial analysis is an active research area in computer vision, with many practical applications. Most of the existing studies focus on addressing one specific task and maximizing its performance. For a complete facial analysis system, one needs to solve these tasks efficiently to ensure a smooth experience. In this work, we present a system-level design of a real-time facial analysis system. With a collection of deep neural networks for object detection, classification, and regression, the system recognizes age, gender, facial expression, and facial similarity for each person that appears in the camera view. We investigate the parallelization and interplay of individual tasks. Results on common off-the-shelf architecture show that the system's accuracy is comparable to the state-of-the-art methods, and the recognition speed satisfies real-time requirements. Moreover, we propose a multitask network for jointly predicting the first three attributes, i.e., age, gender, and facial expression. Source code and trained models are available at https://github.com/mahehu/TUT-live-age-estimator.
Kokoelmat
- TUNICRIS-julkaisut [16951]