MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Frontal Face Images

This paper is aimed at creating extremely small and fast convolutional neural networks (CNN) for the problem of facial expression recognition (FER) from frontal face images. We show that, for this problem, translation invariance (achieved through max-pooling layers) degrades performance, especially when the network is small, and that the knowledge distillation method can be used to obtain extremely compressed CNNs. Extensive comparisons are made on two widely-used FER datasets, CK+ and Oulu-CASIA. In addition, our smallest model (MicroExpNet), obtained using knowledge distillation, is less than $1$MB in size and works at 1408 frames per second on an Intel i7 CPU. MicroExpNet performs on part with our largest model on the CK+ and Oulu-CASIA datasets but with 330x fewer parameters.

Treelogy: A Novel Tree Classifier Utilizing Deep and Hand-crafted Representations

We propose a novel tree classification system called Treelogy, that fuses deep representations with hand-crafted features obtained from leaf images to perform leaf-based plant classification. Key to this system are segmentation of the leaf from an untextured background, using convolutional neural networks (CNNs) for learning deep representations, extracting hand-crafted features with a number of image processing techniques, training a linear SVM with feature vectors, merging SVM and CNN results, and identifying the species from a dataset of 57 trees. Our classification results show that fusion of deep representations with hand-crafted features leads to the highest accuracy. The proposed algorithm is embedded in a smart-phone application, which is publicly available. Furthermore, our novel dataset comprised of 5408 leaf images is also made public for use of other researchers.