A Robust Approach for Smile Recognition via Deep Convolutional Neural Networks

Liu, Yuanzhu; Liu, Zuoli; Zhao, Yong; Xu, Junli

doi:10.1109/icivc55077.2022.9886093

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…This method also has a high speed, but its accuracy decreases when dealing with low-resolution images [16]. In the same year in 2022, Liu et al [17] utilized the MobileNetV2 network for smile detection.…”

Section: Smile Detectionmentioning

confidence: 99%

Deep Multi-task Convolutional Neural Networks for Efficient Classification of Face Attributes

Rohani,

Farsi,

Mohamadzadeh

2023

IJE

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Facial feature recognition is an important subject in computer vision with numerous applications. The human face plays a significant role in social interaction and personology. Valuable information such as identity, age, gender, and emotions can be revealed via facial features. The purpose of this paper is to present a technique for detecting age, smile, and gender from facial images. A multi-task deep learning (MT-DL) framework was proposed that can simultaneously estimate three important features of the human face with remarkable accuracy. Additionally, the proposed approach aims to reduce the number of trainable network parameters while leveraging the combination of features from different layers to increase the overall accuracy. The conducted tests demonstrate that the proposed method outperforms recent advanced techniques in all three accuracy criteria. Moreover, it was demonstrated that multi-task learning (MTL) is capable of improving the accuracy by 1.55% in the smile task, 2.04% in the gender task, and 3.52% in the age task even with less available data, by utilizing tasks with more available data. Furthermore, the trainable parameters of the network in the MTL mode for estimating three tasks simultaneously increase only by about 40% compared to the single-task mode. The proposed method was evaluated on the IMDB-WIKI and GENKI-4K datasets and produced comparable accuracy to the state-of-the-art methods in terms of smile, age detection, and gender classification.

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Section: Smile Detectionmentioning

confidence: 99%