Deep Learning Analysis and Age Prediction from Shoeprints
Jilin University（吉林大学）；Nanyang Technological University（南洋理工大学）；Everspray Science and Technology Company Ltd.（Everspray科技有限公司）；University of Missouri-Columbia（密苏里大学哥伦比亚分校）
Muhammad Hassan, Yan Wang, Di Wang, Daixi Li, Yanchun Liang, You Zhou, Dong Xu
Human walking and gaits involve several complex body parts and are influenced by personality, mood, social and cultural traits, and aging. These factors are reflected in shoeprints, which in turn can be used to predict age, a problem not systematically addressed using any computational approach. We collected 100,000 shoeprints of subjects ranging from 7 to 80 years old and used the data to develop a deep learning end-to-end model ShoeNet to analyze age-related patterns and predict age. The model integrates various convolutional neural network models together using a skip mechanism to extract age-related features, especially in pressure and abrasion regions from pair-wise shoeprints. The results show that 40.23% of the subjects had prediction errors within 5-years of age and the prediction accuracy for gender classification reached 86.07%. Interestingly, the age-related features mostly reside in the asymmetric differences between left and right shoeprints. The analysis also reveals interesting age-related and gender-related patterns in the pressure distributions on shoeprints; in particular, the pressure forces spread from the middle of the toe toward outside regions over age with gender-specific variations on heel regions. Such statistics provide insight into new methods for forensic investigations, medical studies of gait-pattern disorders, biometrics, and sport studies.
Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition
Tencent AI Lab（腾讯人工智能实验室）
随着年龄的增长，人脸的外观会发生显著的类内变化，因此年龄不变人脸识别（AIFR）仍然是人脸识别领域的一个主要挑战。为了减少由年龄引起的类内差异，在本文中，我们提出了一种新的方法（即正交嵌入CNN，或OE-CNN）来学习年龄不变的深层人脸特征。具体来说，我们将深层人脸特征分解为两个正交分量来表示年龄相关和身份相关的特征。因此，对老化具有鲁棒性的身份相关特征随后用于AIFR。此外，为了补充现有的跨年龄人脸数据集，推进该领域的研究，我们构建了一个全新的大规模跨年龄人脸数据集（CAF）。在三个公共领域人脸老化数据集（MORPH Album 2、CACD-VS和FG-NET）上进行的大量实验表明了所提出方法的有效性和所构建的CAF数据集在AIFR上的价值。在最流行的通用人脸识别（GFR）数据集LFW上对我们的算法进行基准测试，还证明了GFR的泛化性能。
Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Prediction
Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology（汉堡工业大学医疗技术与智能系统研究所）；jung diagnostics GmbH（jung diagnostics GmbH公司）；Hamburg University of Technology（汉堡工业大学）
