| 1. |
- Gogic, Ivan, et al.
(författare)
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Fast facial expression recognition using local binary features and shallow neural networks
- 2020
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Ingår i: The Visual Computer. - : SPRINGER. - 0178-2789 .- 1432-2315. ; 36:1, s. 97-112
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Tidskriftsartikel (refereegranskat)abstract
- Facial expression recognition applications demand accurate and fast algorithms that can run in real time on platforms with limited computational resources. We propose an algorithm that bridges the gap between precise but slow methods and fast but less precise methods. The algorithm combines gentle boost decision trees and neural networks. The gentle boost decision trees are trained to extract highly discriminative feature vectors (local binary features) for each basic facial expression around distinct facial landmark points. These sparse binary features are concatenated and used to jointly optimize facial expression recognition through a shallow neural network architecture. The joint optimization improves the recognition rates of difficult expressions such as fear and sadness. Furthermore, extensive experiments in both within- and cross-database scenarios have been conducted on relevant benchmark data sets for facial expression recognition: CK+, MMI, JAFFE, and SFEW 2.0. The proposed method (LBF-NN) compares favorably with state-of-the-art algorithms while achieving an order of magnitude improvement in execution time.
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| 2. |
- Gogic, Ivan, et al.
(författare)
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Regression-based methods for face alignment: A survey
- 2021
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Ingår i: Signal Processing. - : ELSEVIER. - 0165-1684 .- 1872-7557. ; 178
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Forskningsöversikt (refereegranskat)abstract
- Face alignment is the process of determining a face shape given its location and size in an image. It is used as a basis for other facial analysis tasks and for human-machine interaction and augmented reality applications. It is a challenging problem due to the extremely high variability in facial appearance affected by many external (illumination, occlusion, head pose) and internal factors (race, facial expression). However, advances in deep learning combined with domain-related knowledge from previous research recently demonstrated impressive results nearly saturating the unconstrained benchmark data sets. The focus is shifting towards reducing the computational burden of the face alignment models since real-time performance is required for such a highly dynamic task. Furthermore, many applications target devices on the edge with limited computational power which puts even greater emphasis on computational efficiency. We present the latest development in regression-based approaches that have led towards nearly solving the face alignment problem in an unconstrained scenario. Various regression architectures are systematically explored and recent training techniques discussed in the context of face alignment. Finally, a benchmark comparison of the most successful methods is presented, taking into account execution time as well, to provide a comprehensive overview of this dynamic research field. (C) 2020 Elsevier B.V. All rights reserved.
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| 3. |
- Markus, Nenad, et al.
(författare)
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Memory-efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment
- 2018
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Ingår i: Proceedings of BMVC 2018 and Workshops. - Newcastle upon Tyne, UK : The British Machine Vision Association and Society for Pattern Recognition. ; , s. 1-11
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Konferensbidrag (refereegranskat)abstract
- Ren et al. [17] recently introduced a method for aggregating multiple decision trees into a strong predictor by interpreting a path taken by a sample down each tree as a binary vector and performing linear regression on top of these vectors stacked together. They provided experimental evidence that the method offers advantages over the usual approaches for combining decision trees (random forests and boosting). The method truly shines when the regression target is a large vector with correlated dimensions, such as a 2D face shape represented with the positions of several facial landmarks. However, we argue that their basic method is not applicable in many practical scenarios due to large memory requirements. This paper shows how this issue can be solved through the use of quantization and architectural changes of the predictor that maps decision tree-derived encodings to the desired output.
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