4,056 research outputs found
Micro-expression Recognition using Spatiotemporal Texture Map and Motion Magnification
Micro-expressions are short-lived, rapid facial expressions that are exhibited by individuals when they are in high stakes situations. Studying these micro-expressions is important as these cannot be modified by an individual and hence offer us a peek into what the individual is actually feeling and thinking as opposed to what he/she is trying to portray. The spotting and recognition of micro-expressions has applications in the fields of criminal investigation, psychotherapy, education etc. However due to micro-expressions’ short-lived and rapid nature; spotting, recognizing and classifying them is a major challenge. In this paper, we design a hybrid approach for spotting and recognizing micro-expressions by utilizing motion magnification using Eulerian Video Magnification and Spatiotemporal Texture Map (STTM). The validation of this approach was done on the spontaneous micro-expression dataset, CASMEII in comparison with the baseline. This approach achieved an accuracy of 80% viz. an increase by 5% as compared to the existing baseline by utilizing 10-fold cross validation using Support Vector Machines (SVM) with a linear kernel
Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition
Facial micro-expression (ME) recognition has posed a huge challenge to
researchers for its subtlety in motion and limited databases. Recently,
handcrafted techniques have achieved superior performance in micro-expression
recognition but at the cost of domain specificity and cumbersome parametric
tunings. In this paper, we propose an Enriched Long-term Recurrent
Convolutional Network (ELRCN) that first encodes each micro-expression frame
into a feature vector through CNN module(s), then predicts the micro-expression
by passing the feature vector through a Long Short-term Memory (LSTM) module.
The framework contains two different network variants: (1) Channel-wise
stacking of input data for spatial enrichment, (2) Feature-wise stacking of
features for temporal enrichment. We demonstrate that the proposed approach is
able to achieve reasonably good performance, without data augmentation. In
addition, we also present ablation studies conducted on the framework and
visualizations of what CNN "sees" when predicting the micro-expression classes.Comment: Published in Micro-Expression Grand Challenge 2018, Workshop of 13th
IEEE Facial & Gesture 201
Biometric Authentication System on Mobile Personal Devices
We propose a secure, robust, and low-cost biometric authentication system on the mobile personal device for the personal network. The system consists of the following five key modules: 1) face detection; 2) face registration; 3) illumination normalization; 4) face verification; and 5) information fusion. For the complicated face authentication task on the devices with limited resources, the emphasis is largely on the reliability and applicability of the system. Both theoretical and practical considerations are taken. The final system is able to achieve an equal error rate of 2% under challenging testing protocols. The low hardware and software cost makes the system well adaptable to a large range of security applications
Facial Expression Recognition from World Wild Web
Recognizing facial expression in a wild setting has remained a challenging
task in computer vision. The World Wide Web is a good source of facial images
which most of them are captured in uncontrolled conditions. In fact, the
Internet is a Word Wild Web of facial images with expressions. This paper
presents the results of a new study on collecting, annotating, and analyzing
wild facial expressions from the web. Three search engines were queried using
1250 emotion related keywords in six different languages and the retrieved
images were mapped by two annotators to six basic expressions and neutral. Deep
neural networks and noise modeling were used in three different training
scenarios to find how accurately facial expressions can be recognized when
trained on noisy images collected from the web using query terms (e.g. happy
face, laughing man, etc)? The results of our experiments show that deep neural
networks can recognize wild facial expressions with an accuracy of 82.12%
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