Multithreading cascade of SURF for facial expression recognition

Chen, Jinhui; Luo, Zhaojie; Takiguchi, Tetsuya; Ariki, Yasuo

doi:10.1186/s13640-016-0140-7

Cited by 8 publications

(7 citation statements)

References 33 publications

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“…To learn the Oi-HOG features, we use a multithreading cascade algorithm, which appears in our previous publication [41] and the training procedure also follows the setting reported in our previous publication. The novel classification framework in the study reported in this paper is referred to as multithreading cascade of orientation-invariant histograms for oriented gradients (McOiHOG).…”

Section: B Results Comparisonmentioning

confidence: 99%

“…Adopting the pre-calculated polar templates, the convergence rate for feature learning was improved by 73 minutes. To make fair comparison, we used the same learning framework and the same data (multithreading cascade [41]) for learning Oi-HOG, HOG, SURF, SIFT, and Haar features. The results are shown in Table I.…”

Section: B Results Comparisonmentioning

confidence: 99%

“…To explore their feature orientation invariance, we have evaluated and compared several existing state-of-the-art learning Table II, where the best one (MC) was used to construct the proposed feature learning framework for comparison with the state-of-the-art in Tables V and VI. The top performers are BinBoost [43], joint cascade (JC) [44], soft cascade (SC) [45] and multithreading cascade (MC) [41].…”

Section: B Results Comparisonmentioning

confidence: 99%

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Polar Transformation on Image Features for Orientation-Invariant Representations

Chen

Luo

Huang

et al. 2019

IEEE Trans. Multimedia

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The choice of image feature representation plays a crucial role in the analysis of visual information. Although vast numbers of alternative robust feature representation models have been proposed to improve the performance of different visual tasks, most existing feature representations (e.g. handcrafted features or Convolutional Neural Networks (CNN)) have a relatively limited capacity to capture the highly orientationinvariant (rotation/reversal) features. The net consequence is suboptimal visual performance. To address these problems, this study adopts a novel transformational approach, which investigates the potential of using polar feature representations. Our low level consists of a histogram of oriented gradient, which is then binned using annular spatial bin-type cells applied to the polar gradient. This gives gradient binning invariance for feature extraction. In this way, the descriptors have significantly enhanced orientation-invariant capabilities. The proposed feature representation, termed orientation-invariant histograms of oriented gradients (Oi-HOG), is capable of accurately processing visual tasks (e.g., facial expression recognition). In the context of the CNN architecture, we propose two polar convolution operations, referred to as Full Polar Convolution (FPolarConv) and Local Polar Convolution (LPolarConv), and use these to develop polar architectures for the CNN orientation-invariant representation. Experimental results show that the proposed orientation-invariant image representation, based on polar models for both handcrafted features and deep learning features, is both competitive with state-of-the-art methods and maintains a compact representation on a set of challenging benchmark image datasets. Index Terms-Rotation-invariant and reversal-invariant representation, HOG, CNN.

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Section: B Results Comparisonmentioning

confidence: 99%

Section: B Results Comparisonmentioning

confidence: 99%

Section: B Results Comparisonmentioning

confidence: 99%

See 1 more Smart Citation

Polar Transformation on Image Features for Orientation-Invariant Representations

Chen

Luo

Huang

et al. 2019

IEEE Trans. Multimedia

Self Cite

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“…Specifically, the proposed model must extract the parallel speech data, which can limit the process to a oneto-one conversion only. In future works, we will explore a many-to-many emotional VC method and use it in other applications, such as emotional voice recognition [25] or facial expression recognition [26].…”

Section: Discussionmentioning

confidence: 99%

Emotional voice conversion using neural networks with arbitrary scales F0 based on wavelet transform

Luo

Chen

Takiguchi

et al. 2017

J AUDIO SPEECH MUSIC PROC.

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An artificial neural network is an important model for training features of voice conversion (VC) tasks. Typically, neural networks (NNs) are very effective in processing nonlinear features, such as Mel Cepstral Coefficients (MCC), which represent the spectrum features. However, a simple representation of fundamental frequency (F0) is not enough for NNs to deal with emotional voice VC. This is because the time sequence of F0 for an emotional voice changes drastically. Therefore, in our previous method, we used the continuous wavelet transform (CWT) to decompose F0 into 30 discrete scales, each separated by one third of an octave, which can be trained by NNs for prosody modeling in emotional VC. In this study, we propose the arbitrary scales CWT (AS-CWT) method to systematically capture F0 features of different temporal scales, which can represent different prosodic levels ranging from micro-prosody to sentence levels. Meanwhile, the proposed method uses deep belief networks (DBNs) to pre-train the NNs that then convert spectral features. By utilizing these approaches, the proposed method can change the spectrum and the F0 for an emotional voice simultaneously as well as outperform other state-of-the-art methods in terms of emotional VC.

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“…On the contrary, appearance-based features extraction methods encode the face appearance variations without taking muscle motion into account. Chen et al [10] introduced the multithreading cascade of Speeded Up Robust Features (McSURF), which improve the recognition accuracy rate. Cruz et al [11] explore the temporal derivative and adjacent frames by using new framework known as temporal patterns of oriented edge magnitudes.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Facial Expressions under Varying Conditions Using Dual‐Feature Fusion

Mahmood

Hussain

Iqbal

et al. 2019

Mathematical Problems in Engineering

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Facial expression recognition plays an important role in communicating the emotions and intentions of human beings. Facial expression recognition in uncontrolled environment is more difficult as compared to that in controlled environment due to change in occlusion, illumination, and noise. In this paper, we present a new framework for effective facial expression recognition from real-time facial images. Unlike other methods which spend much time by dividing the image into blocks or whole face image, our method extracts the discriminative feature from salient face regions and then combine with texture and orientation features for better representation. Furthermore, we reduce the data dimension by selecting the highly discriminative features. The proposed framework is capable of providing high recognition accuracy rate even in the presence of occlusions, illumination, and noise. To show the robustness of the proposed framework, we used three publicly available challenging datasets. The experimental results show that the performance of the proposed framework is better than existing techniques, which indicate the considerable potential of combining geometric features with appearance-based features.

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Multithreading cascade of SURF for facial expression recognition

Cited by 8 publications

References 33 publications

Polar Transformation on Image Features for Orientation-Invariant Representations

Polar Transformation on Image Features for Orientation-Invariant Representations

Emotional voice conversion using neural networks with arbitrary scales F0 based on wavelet transform

Recognition of Facial Expressions under Varying Conditions Using Dual‐Feature Fusion

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