2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDIVMSP-12.6
Paper Title Deep and Ordinal Ensemble Learning for Human Age Estimation From Facial Images
Authors Jiu-Cheng Xie, Chi-Man Pun, University of Macau, Macau SAR China
SessionIVMSP-12: Image & Video Interpretation and Understanding
LocationGather.Town
Session Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Poster
Topic Image, Video, and Multidimensional Signal Processing: [IVARS] Image & Video Analysis, Synthesis, and Retrieval
Abstract In this paper, we partially embrace the decomposition idea and propose the Deep and Ordinal Ensemble Learning with Two Groups Classification (DOEL 2groups ) for age prediction. Specifically, we first set up the ensemble based on Convolutional Neural Network (CNN) techniques, while the ordinal relationship is implicitly constructed among its base learners. Each base learner will classify the target face into one of two specific age groups. After achieving probability predictions of different age groups, then we make aggregation by transforming them into counting value distributions of whole age classes and getting the final age estimation from their votes. Moreover, to further improve the estimation performance, we suggest to regard the age class at the boundary of original two age groups as another age group and this modified version is named the Deep and Ordinal Ensemble Learning with Three Groups Classification (DOEL 3groups ). Effectiveness of this new grouping scheme is validated in theory and practice. Finally, we evaluate the proposed two ensemble methods on controlled and wild aging databases, and both of them produce competitive results. Note that the DOEL 3groups shows the state-of-the-art performance in most cases.