2 research outputs found
FoodNet: Recognizing Foods Using Ensemble of Deep Networks
In this work we propose a methodology for an automatic food classification
system which recognizes the contents of the meal from the images of the food.
We developed a multi-layered deep convolutional neural network (CNN)
architecture that takes advantages of the features from other deep networks and
improves the efficiency. Numerous classical handcrafted features and approaches
are explored, among which CNNs are chosen as the best performing features.
Networks are trained and fine-tuned using preprocessed images and the filter
outputs are fused to achieve higher accuracy. Experimental results on the
largest real-world food recognition database ETH Food-101 and newly contributed
Indian food image database demonstrate the effectiveness of the proposed
methodology as compared to many other benchmark deep learned CNN frameworks.Comment: 5 pages, 3 figures, 3 tables, IEEE Signal Processing Letter
Distinguishing Posed and Spontaneous Smiles by Facial Dynamics
Smile is one of the key elements in identifying emotions and present state of
mind of an individual. In this work, we propose a cluster of approaches to
classify posed and spontaneous smiles using deep convolutional neural network
(CNN) face features, local phase quantization (LPQ), dense optical flow and
histogram of gradient (HOG). Eulerian Video Magnification (EVM) is used for
micro-expression smile amplification along with three normalization procedures
for distinguishing posed and spontaneous smiles. Although the deep CNN face
model is trained with large number of face images, HOG features outperforms
this model for overall face smile classification task. Using EVM to amplify
micro-expressions did not have a significant impact on classification accuracy,
while the normalizing facial features improved classification accuracy. Unlike
many manual or semi-automatic methodologies, our approach aims to automatically
classify all smiles into either `spontaneous' or `posed' categories, by using
support vector machines (SVM). Experimental results on large UvA-NEMO smile
database show promising results as compared to other relevant methods.Comment: 16 pages, 8 figures, ACCV 2016, Second Workshop on Spontaneous Facial
Behavior Analysi