Age Group Classifier of Adults and Children with YOLO-based Deep Learning Pre-Processing Scheme for Embedded Platforms

Based on the information of body proportion, in this study, a simple and effective processing scheme is developed for two age groups classification, i.e. children and adults for the applications of smart autonomous movers. By the YOLO-based CNN model for head and body objects detections, the recogni...

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Bibliographic Details
Published in2022 IEEE 12th International Conference on Consumer Electronics (ICCE-Berlin) pp. 1 - 3
Main Authors Lin, Jie-Min, Lin, Wei-Liang, Fan, Chih-Peng
Format Conference Proceeding
LanguageEnglish
Published IEEE 02.09.2022
Subjects
Adults and children recognition
Deep learning
Design methodology
Face recognition
Head
Image recognition
Object detection
Real-time systems
smart autonomous mover
YOLO model
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Summary:Based on the information of body proportion, in this study, a simple and effective processing scheme is developed for two age groups classification, i.e. children and adults for the applications of smart autonomous movers. By the YOLO-based CNN model for head and body objects detections, the recognition accuracies of age group classification for children and adults are 95% and 92.5% respectively with the image datasets collected in publics. Compared with the existed design, the proposed methodology performs simpler and more effective recognition capability for age group classification of adults and children. The proposed design is implemented on GPU-based embedded platform for real-time applications.
ISSN:2166-6822
DOI:10.1109/ICCE-Berlin56473.2022.9937129