5,326 research outputs found
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active
research topic and has been successfully applied to various Artificial
Intelligence (AI) domains. As a potentially crucial technique for the
development of the next generation of emotional AI systems, we herein provide a
comprehensive overview of the application of adversarial training to affective
computing and sentiment analysis. Various representative adversarial training
algorithms are explained and discussed accordingly, aimed at tackling diverse
challenges associated with emotional AI systems. Further, we highlight a range
of potential future research directions. We expect that this overview will help
facilitate the development of adversarial training for affective computing and
sentiment analysis in both the academic and industrial communities
Positive Semidefinite Metric Learning Using Boosting-like Algorithms
The success of many machine learning and pattern recognition methods relies
heavily upon the identification of an appropriate distance metric on the input
data. It is often beneficial to learn such a metric from the input training
data, instead of using a default one such as the Euclidean distance. In this
work, we propose a boosting-based technique, termed BoostMetric, for learning a
quadratic Mahalanobis distance metric. Learning a valid Mahalanobis distance
metric requires enforcing the constraint that the matrix parameter to the
metric remains positive definite. Semidefinite programming is often used to
enforce this constraint, but does not scale well and easy to implement.
BoostMetric is instead based on the observation that any positive semidefinite
matrix can be decomposed into a linear combination of trace-one rank-one
matrices. BoostMetric thus uses rank-one positive semidefinite matrices as weak
learners within an efficient and scalable boosting-based learning process. The
resulting methods are easy to implement, efficient, and can accommodate various
types of constraints. We extend traditional boosting algorithms in that its
weak learner is a positive semidefinite matrix with trace and rank being one
rather than a classifier or regressor. Experiments on various datasets
demonstrate that the proposed algorithms compare favorably to those
state-of-the-art methods in terms of classification accuracy and running time.Comment: 30 pages, appearing in Journal of Machine Learning Researc
3D Face Recognition: Feature Extraction Based on Directional Signatures from Range Data and Disparity Maps
In this paper, the author presents a work on i) range data and ii) stereo-vision system based disparity map profiling that are used as signatures for 3D face recognition. The signatures capture the intensity variations along a line at sample points on a face in any particular direction. The directional signatures and some of their combinations are compared to study the variability in recognition performances. Two 3D face image datasets namely, a local student database captured with a stereo vision system and the FRGC v1 range dataset are used for performance evaluation
GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks
Facial landmarks constitute the most compressed representation of faces and
are known to preserve information such as pose, gender and facial structure
present in the faces. Several works exist that attempt to perform high-level
face-related analysis tasks based on landmarks. In contrast, in this work, an
attempt is made to tackle the inverse problem of synthesizing faces from their
respective landmarks. The primary aim of this work is to demonstrate that
information preserved by landmarks (gender in particular) can be further
accentuated by leveraging generative models to synthesize corresponding faces.
Though the problem is particularly challenging due to its ill-posed nature, we
believe that successful synthesis will enable several applications such as
boosting performance of high-level face related tasks using landmark points and
performing dataset augmentation. To this end, a novel face-synthesis method
known as Gender Preserving Generative Adversarial Network (GP-GAN) that is
guided by adversarial loss, perceptual loss and a gender preserving loss is
presented. Further, we propose a novel generator sub-network UDeNet for GP-GAN
that leverages advantages of U-Net and DenseNet architectures. Extensive
experiments and comparison with recent methods are performed to verify the
effectiveness of the proposed method.Comment: 6 pages, 5 figures, this paper is accepted as 2018 24th International
Conference on Pattern Recognition (ICPR2018
Time-Efficient Hybrid Approach for Facial Expression Recognition
Facial expression recognition is an emerging research area for improving human and computer interaction. This research plays a significant role in the field of social communication, commercial enterprise, law enforcement, and other computer interactions. In this paper, we propose a time-efficient hybrid design for facial expression recognition, combining image pre-processing steps and different Convolutional Neural Network (CNN) structures providing better accuracy and greatly improved training time. We are predicting seven basic emotions of human faces: sadness, happiness, disgust, anger, fear, surprise and neutral. The model performs well regarding challenging facial expression recognition where the emotion expressed could be one of several due to their quite similar facial characteristics such as anger, disgust, and sadness. The experiment to test the model was conducted across multiple databases and different facial orientations, and to the best of our knowledge, the model provided an accuracy of about 89.58% for KDEF dataset, 100% accuracy for JAFFE dataset and 71.975% accuracy for combined (KDEF + JAFFE + SFEW) dataset across these different scenarios. Performance evaluation was done by cross-validation techniques to avoid bias towards a specific set of images from a database
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