9,496 research outputs found
Review of Face Detection Systems Based Artificial Neural Networks Algorithms
Face detection is one of the most relevant applications of image processing
and biometric systems. Artificial neural networks (ANN) have been used in the
field of image processing and pattern recognition. There is lack of literature
surveys which give overview about the studies and researches related to the
using of ANN in face detection. Therefore, this research includes a general
review of face detection studies and systems which based on different ANN
approaches and algorithms. The strengths and limitations of these literature
studies and systems were included also.Comment: 16 pages, 12 figures, 1 table, IJMA Journa
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
MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes
Attribute recognition, particularly facial, extracts many labels for each
image. While some multi-task vision problems can be decomposed into separate
tasks and stages, e.g., training independent models for each task, for a
growing set of problems joint optimization across all tasks has been shown to
improve performance. We show that for deep convolutional neural network (DCNN)
facial attribute extraction, multi-task optimization is better. Unfortunately,
it can be difficult to apply joint optimization to DCNNs when training data is
imbalanced, and re-balancing multi-label data directly is structurally
infeasible, since adding/removing data to balance one label will change the
sampling of the other labels. This paper addresses the multi-label imbalance
problem by introducing a novel mixed objective optimization network (MOON) with
a loss function that mixes multiple task objectives with domain adaptive
re-weighting of propagated loss. Experiments demonstrate that not only does
MOON advance the state of the art in facial attribute recognition, but it also
outperforms independently trained DCNNs using the same data. When using facial
attributes for the LFW face recognition task, we show that our balanced (domain
adapted) network outperforms the unbalanced trained network.Comment: Post-print of manuscript accepted to the European Conference on
Computer Vision (ECCV) 2016
http://link.springer.com/chapter/10.1007%2F978-3-319-46454-1_
Linguistically-driven framework for computationally efficient and scalable sign recognition
We introduce a new general framework for sign recognition from monocular video using limited quantities of annotated data. The novelty of the hybrid framework we describe here is that we exploit state-of-the art learning methods while also incorporating features based on what we know about the linguistic composition of lexical signs. In particular, we analyze hand shape, orientation, location, and motion trajectories, and then use CRFs to combine this linguistically significant information for purposes of sign recognition. Our robust modeling and recognition of these sub-components of sign production allow an efficient parameterization of the sign recognition problem as compared with purely data-driven methods. This parameterization enables a scalable and extendable time-series learning approach that advances the state of the art in sign recognition, as shown by the results reported here for recognition of isolated, citation-form, lexical signs from American Sign Language (ASL)
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