2,735 research outputs found
Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition
In the recent year, state-of-the-art for facial micro-expression recognition
have been significantly advanced by deep neural networks. The robustness of
deep learning has yielded promising performance beyond that of traditional
handcrafted approaches. Most works in literature emphasized on increasing the
depth of networks and employing highly complex objective functions to learn
more features. In this paper, we design a Shallow Triple Stream
Three-dimensional CNN (STSTNet) that is computationally light whilst capable of
extracting discriminative high level features and details of micro-expressions.
The network learns from three optical flow features (i.e., optical strain,
horizontal and vertical optical flow fields) computed based on the onset and
apex frames of each video. Our experimental results demonstrate the
effectiveness of the proposed STSTNet, which obtained an unweighted average
recall rate of 0.7605 and unweighted F1-score of 0.7353 on the composite
database consisting of 442 samples from the SMIC, CASME II and SAMM databases.Comment: 5 pages, 1 figure, Accepted and published in IEEE FG 201
Improvements of local directional pattern for texture classification.
Doctoral Degree. University of KwaZulu-Natal, Durban.The Local Directional Pattern (LDP) method has established its effectiveness and performance compared to the popular Local Binary Pattern (LBP) method in different applications. In this thesis, several extensions and modification of LDP are proposed with an objective to increase its robustness and discriminative power. Local Directional Pattern (LDP) is
dependent on the empirical choice of three for the number of significant bits used to code the responses of the Kirsch Mask operation. In a first study, we applied LDP on informal settlements using various values for the number of significant bits k. It was observed that the change of the value of the number of significant bits led to a change in the performance, depending on the application. Local Directional Pattern (LDP) is based on the computation Kirsch Mask application response
values in eight directions. But this method ignores the gray value of the center pixel, which may lead to loss of significant information. Centered Local Directional Pattern (CLDP) is introduced to solve this issue, using the value of the center pixel based on its relations with neighboring pixels. Local Directional Pattern (LDP) also generates a code based on the
absolute value of the edge response value; however, the sign of the original value indicates two different trends (positive or negative) of the gradient. To capture the gradient trend, Signed Local Directional Pattern (SLDP) and Centered-SLDP (C-SLDP) are proposed, which compute the eight edge responses based on the two different directions (positive or negative)
of the gradients.The Directional Local Binary pattern (DLBP) is introduced, which adopts directional information to represent texture images. This method is more stable than both LDP and LBP because it utilizes the center pixel as a threshold for the edge response of a pixel in eight directions, instead of employing the center pixel as the threshold for pixel intensity of the neighbors, as in the LBP method. Angled Local directional pattern (ALDP) is also presented, with an objective to resolve two problems in the LDP method. These are the value of the number of significant bits k, and to taking into account the center pixel value. It computes the angle values for the edge response of a pixel in eight directions for each angle (0â—¦,45â—¦,90â—¦,135â—¦).
Each angle vector contains three values. The central value in each vector is chosen as a threshold for the other two neighboring pixels. Circular Local Directional Pattern (CILDP) isalso presented, with an objective of a better analysis, especially with textures with a different scale. The method is built around the circular shape to compute the directional edge vector
using different radiuses.
The performances of LDP, LBP, CLDP, SLDP, C-SLDP, DLBP, ALDP and CILDP are evaluated using five classifiers (K-nearest neighbour algorithm (k-NN), Support Vector Machine (SVM), Perceptron, Naive-Bayes (NB), and Decision Tree (DT)) applied to two different texture datasets: Kylberg dataset and KTH-TIPS2-b dataset. The experimental results demonstrated that the proposed methods outperform both LDP and LBP
Face Analysis Using Row and Correlation Based Local Directional Pattern
Face analysis, which includes face recognition and facial expression recognition, has been attempted by many researchers and gave ideal solutions. The problem is still active and challenging due to an increase in the complexity of the problem viz. due to poor lighting, face occlusion, low-resolution images, etc. Local pattern descriptor methods introduced to overcome these critical issues and improve the recognition rate. These methods extract the discriminant information from the local features of the face image for recognition. In this paper, the local descriptor based two methods, namely row-based local directional pattern and correlation-based local directional pattern proposed by extending an existing descriptor -- local directional pattern (LDP). Further, the two feature vectors obtained by these methods concatenated to form a hybrid descriptor. Experimentation has carried out on benchmark databases and results infer that the proposed hybrid descriptor outperforms the other descriptors in face analysis
Video Interpolation using Optical Flow and Laplacian Smoothness
Non-rigid video interpolation is a common computer vision task. In this paper
we present an optical flow approach which adopts a Laplacian Cotangent Mesh
constraint to enhance the local smoothness. Similar to Li et al., our approach
adopts a mesh to the image with a resolution up to one vertex per pixel and
uses angle constraints to ensure sensible local deformations between image
pairs. The Laplacian Mesh constraints are expressed wholly inside the optical
flow optimization, and can be applied in a straightforward manner to a wide
range of image tracking and registration problems. We evaluate our approach by
testing on several benchmark datasets, including the Middlebury and Garg et al.
datasets. In addition, we show application of our method for constructing 3D
Morphable Facial Models from dynamic 3D data
Less is More: Micro-expression Recognition from Video using Apex Frame
Despite recent interest and advances in facial micro-expression research,
there is still plenty room for improvement in terms of micro-expression
recognition. Conventional feature extraction approaches for micro-expression
video consider either the whole video sequence or a part of it, for
representation. However, with the high-speed video capture of micro-expressions
(100-200 fps), are all frames necessary to provide a sufficiently meaningful
representation? Is the luxury of data a bane to accurate recognition? A novel
proposition is presented in this paper, whereby we utilize only two images per
video: the apex frame and the onset frame. The apex frame of a video contains
the highest intensity of expression changes among all frames, while the onset
is the perfect choice of a reference frame with neutral expression. A new
feature extractor, Bi-Weighted Oriented Optical Flow (Bi-WOOF) is proposed to
encode essential expressiveness of the apex frame. We evaluated the proposed
method on five micro-expression databases: CAS(ME), CASME II, SMIC-HS,
SMIC-NIR and SMIC-VIS. Our experiments lend credence to our hypothesis, with
our proposed technique achieving a state-of-the-art F1-score recognition
performance of 61% and 62% in the high frame rate CASME II and SMIC-HS
databases respectively.Comment: 14 pages double-column, author affiliations updated, acknowledgment
of grant support adde
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