Devising computational models for detecting abnormalities reflective of diseases from facial structures is a novel and emerging field of research in automatic face analysis. In this paper, we focus on automatic pain intensity estimation from faces. This has a paramount potential diagnosis values in healthcare applications. In this context, we present a novel 3D deep model for dynamic spatiotemporal representation of faces in videos. Using several convolutional layers with diverse temporal depths, our proposed model captures a wide range of spatiotemporal variations in the faces. Moreover, we introduce a cross-architecture knowledge transfer technique for training 3D convolutional neural networks using a pre-trained 2D architecture. This strategy is a practical approach for training 3D models, especially when the size of the database is relatively small. Our extensive experiments and analysis on two benchmarking and publicly available databases, namely the UNBC-McMaster shoulder pain and the BioVid, clearly show that our proposed method consistently outperforms many state-of-the-art methods in automatic pain intensity estimation.
Automatic pain assessment is crucial in clinical diagnosis. Experiencing pain causes deformations in the facial structure resulting in different spontaneous facial expressions. In this paper, we aim to represent the facial expressions as a compact binary code for classification of different pain intensity levels. We divide a given face video into non-overlapping equal-length segments. Using a Convolutional Neural Network (CNN), we extract features from randomly sampled frames from all segments. The obtained features are aggregated by exploiting statistics to incorporate low-level visual patterns and high-level structural information. Finally, this processed information is encoded using a deep network to obtain a single binary code such that videos with the same pain intensity level have smaller Hamming distance than those of different levels. Extensive experiments on the publicly available UNBC-McMaster database demonstrates that our proposed method achieves superior performance compared to the state-of-the-art.
The capability of incrementally learning new classes and learning from a few examples is one of the hallmarks of human intelligence. It is crucial to endow a practical recognition system with such ability. Therefore, in this paper, we conduct pioneering work and focus on a challenging yet practical Semi-Supervised Few-Shot Class-Incremental Learning (SSFSCIL) problem, which requires CNN models incrementally learn new classes from very few labeled samples and a large number of unlabeled samples, without forgetting the previously learned ones. To address this problem, a simple and efficient solution for SSFSCIL is proposed to learn novel categories using a self-training strategy in a semi-supervised manner and avoid catastrophic forgetting by distillation-based methods. Our extensive experiments on CIFAR100, miniImageNet and CUB200 datasets demonstrate the promising performance of our proposed method, and define baselines in this new research direction.
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