Deep learning solution for children long-term identification
2020
Bumanis, N., Latvia Univ. of Life Sciences and Technologies, Jelgava (Latvia) | Vitols, G., WeAreDots Ltd., Riga (Latvia) | Arhipova, I., WeAreDots Ltd., Riga (Latvia) | Meirane, I., WeAreDots Ltd., Riga (Latvia)
Deep learning algorithms are becoming default solution for application in business processes where recognition, identification and automated learning are involved. For human identification, analysis of various features can be applied. Face feature analysis is most popular method for identification of person in various stages of life, including children and infants. The aim of this research was to propose deep learning solution for long-term identification of children in educational institutions. Previously proposed conceptual model for long-term re-identification was enhanced. The enhancements include processing of unexpected persons’ scenarios, knowledge base improvements based on results of supervised and unsupervised learning, implementation of video surveillance zones within educational institutions and object tracking results’ data chaining between multiple logical processes. Object tracking results are the solution we found for long-term identification realization.
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