Face Detection on Mobile: Five Implementations and Analysis
- URL: http://arxiv.org/abs/2205.05572v2
- Date: Thu, 12 May 2022 15:36:35 GMT
- Title: Face Detection on Mobile: Five Implementations and Analysis
- Authors: Kostiantyn Khabarlak
- Abstract summary: We adapt 5 algorithms to mobile, including Viola-Jones (Haar cascade), LBP, HOG, MTCNN, BlazeFace.
We provide guidance, which algorithms are the best fit for mobile face access control systems and potentially other mobile applications.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In many practical cases face detection on smartphones or other highly
portable devices is a necessity. Applications include mobile face access
control systems, driver status tracking, emotion recognition, etc. Mobile
devices have limited processing power and should have long-enough battery life
even with face detection application running. Thus, striking the right balance
between algorithm quality and complexity is crucial. In this work we adapt 5
algorithms to mobile. These algorithms are based on handcrafted or
neural-network-based features and include: Viola-Jones (Haar cascade), LBP,
HOG, MTCNN, BlazeFace. We analyze inference time of these algorithms on
different devices with different input image resolutions. We provide guidance,
which algorithms are the best fit for mobile face access control systems and
potentially other mobile applications. Interestingly, we note that cascaded
algorithms perform faster on scenes without faces, while BlazeFace is slower on
empty scenes. Exploiting this behavior might be useful in practice.
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