Feature fusion for facial landmark detection

Perakis, Panagiotis; Theoharis, Theoharis; Kakadiaris, Ioannis A.

doi:10.1016/j.patcog.2014.03.007

Cited by 34 publications

(28 citation statements)

References 22 publications

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“…The experiments conducted on the Oxford dataset [2] show that the proposed method improves the results of the individual components fused, up to a 10% in the best case. In addition, our method compares favorably with the standard fusion approaches presented in [12].…”

Section: Introductionmentioning

confidence: 91%

“…al [12] evaluates the fusion of 2D and 3D features in the context of facial landmark detection. In a first step, landmarks are learned from an annotated dataset, and later, a template matching approach is applied for recognition.…”

Section: Keypoint Fusionmentioning

confidence: 99%

“…al [12] for keypoint descriptor fusion, adapted to our work. Although their work is based on facial landmark detection, their approach is a general method valid for any application requiring fusion of descriptors.…”

Section: Standard Feature Fusion Approachesmentioning

confidence: 99%

“…After presenting in Section 2 the related works, Section 3 provides a general formulation of the keypoint descriptor fusion problem. Section 4 presents the standard methods for fusion employed in [12] which are compared to our method. Then, Section 5 introduces DST and our proposal.…”

Section: Introductionmentioning

confidence: 99%

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Keypoint descriptor fusion with Dempster–Shafer theory

Mondéjar-Guerra

Muñoz-Salinas

Marín-Jiménez

et al. 2015

International Journal of Approximate Reasoning

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Keypoint matching is the task of accurately finding the location of a scene point in two images. Many keypoint descriptors have been proposed in the literature aiming at providing robustness against scale, translation and rotation transformations, each having advantages and disadvantages. This paper proposes a novel approach to fuse the information from multiple keypoint descriptors using Dempster-Shafer Theory of evidence [1], which has proven particularly efficient in combining sources of information providing incomplete, imprecise, biased, and conflictive knowledge. The matching results of each descriptor are transformed into an evidence distribution on which a confidence factor is computed making use of its entropy. Then, the evidence distributions are fused using Dempster-Shafer Theory (DST), considering its confidence. As result of the fusion, a new evidence distribution that improves the result of the best descriptor is obtained. Our method has been tested with SIFT, SURF, ORB, BRISK and FREAK descriptors using all possible combinations of them. Results on the Oxford keypoint dataset [2] shows that the proposed approach obtains an improvement of up to 10% compared to the best one (FREAK).

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Section: Introductionmentioning

confidence: 91%

Section: Keypoint Fusionmentioning

confidence: 99%

Section: Standard Feature Fusion Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Keypoint descriptor fusion with Dempster–Shafer theory

Mondéjar-Guerra

Muñoz-Salinas

Marín-Jiménez

et al. 2015

International Journal of Approximate Reasoning

View full text Add to dashboard Cite

show abstract

“…There have been many recent achievements in related research sub-areas such as facial landmark localization [3][4] [5][6] [7] [8], tracking and recognition [9] [10]. Realistic facial expression synthesis is useful for affective computing, human computer interaction [11] [12], realistic computer animation [13] and facial surgery planning [14][15], etc.…”

Section: Introductionmentioning

confidence: 99%

Facial expression transfer method based on frequency analysis

Wei

Tian

Maybank

et al. 2016

Pattern Recognition

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Abstract. We propose a novel expression transfer method based on an analysis of the frequency of multi-expression facial images. We locate the facial features automatically and describe the shape deformations between a neutral expression and non-neutral expressions. The subtle expression changes are important visual clues to distinguish different expressions. These changes are more salient in the frequency domain than in the image domain. We extract the subtle local expression deformations for the source subject, coded in the wavelet decomposition. This information about expressions is transferred to a target subject. The resulting synthesized image preserves both the facial appearance of the target subject and the expression details of the source subject.This method is extended to dynamic expression transfer to allow a more precise interpretation of facial expressions.Experiments on Japanese Female Facial Expression (JAFFE), the extended Cohn-Kanade (CK+) and PIE facial expression databases show the superiority of our method over the state-of-the-art method.

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Computer‐aided diagnosis of retinal diseases using multidomain feature fusion

Keerthiveena

Esakkirajan

Selvakumar

et al. 2019

Int J Imaging Syst Tech

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In this article, an attempt is made to combine both transform and spatial domain features to increase the accuracy, sensitivity, and specificity for the classification of retinal diseases. Papilloedema, macular edema, glaucoma, diabetic retinopathy (DR), and central retinal vein occlusion (CRVO) are the leading cause of blindness in humans. To identify these diseases in a mass screening process, it consumes intense labor and time. In this proposed work, an automated computer aided algorithm is developed to classify the normal from the abnormal (Papilledema, macular edema, glaucoma, DR, and CRVO) images. For any classification task, an increase in data size, number of classes, and dimension of the feature space affect the performance of any classifier.A single classifier is generally unable to handle the wide variability and scalability of the data in any problem. In the proposed algorithm, the feature fusion technique is employed to reduce the input data to the classifier. The features are extracted from wavelet packet transform (WPT) and intensity hue saturation (IHS). A support vector machine (SVM)-based learning algorithm is used to train the classifier using the extracted features. The proposed method yields better results than the existing classifier fusion method with an overall accuracy, sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV), and area under ROC curve (AUC) of 96.3%, 95.8%, 97.8%, 96.4%,98.1%, and 0.971 respectively. K E Y W O R D Sfeature fusion, intensity hue saturation, support vector machine, wavelet packet transform, weighted principal component analysis

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Feature fusion for facial landmark detection

Cited by 34 publications

References 22 publications

Keypoint descriptor fusion with Dempster–Shafer theory

Keypoint descriptor fusion with Dempster–Shafer theory

Facial expression transfer method based on frequency analysis

Computer‐aided diagnosis of retinal diseases using multidomain feature fusion

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