Analysis of Local Binary Patterns for face recognition under varying facial expressions

Khorsheed, Jeeman Ahmed; Yurtkan, Kamil

doi:10.1109/siu.2016.7496182

Cited by 11 publications

(8 citation statements)

References 7 publications

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“…In our opinion, authors should use a Gaussian filter before applying LoG, since the combination of these two algorithms would provide better results than the ones obtained. Following the same way, authors in [12,13] also used LBP technique. In [12], the face recognition performance of LBP is investigated under different facial expressions, which are anger, disgust, fear, happiness, sadness, and surprise.…”

Section: Prior Workmentioning

confidence: 99%

“…Following the same way, authors in [12,13] also used LBP technique. In [12], the face recognition performance of LBP is investigated under different facial expressions, which are anger, disgust, fear, happiness, sadness, and surprise. Facial expression deformations are challenging for a robust face recognition system; thus, the study gives an idea about using LBP features to expression invariant.…”

Section: Prior Workmentioning

confidence: 99%

“…LBP (Chi-Square Distance) [12] 82.33 LOG & LBP [3] 80 Lower-Order PZM [10] 97.75 Proposed Method 99.26 Table 3. Unconstrained environment.…”

Section: Accuracy (%)mentioning

confidence: 99%

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Enhancing Face Identification Using Local Binary Patterns and K-Nearest Neighbors

Beli¹,

Guo

2017

J. Imaging

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Abstract:The human face plays an important role in our social interaction, conveying people's identity. Using the human face as a key to security, biometric passwords technology has received significant attention in the past several years due to its potential for a wide variety of applications. Faces can have many variations in appearance (aging, facial expression, illumination, inaccurate alignment and pose) which continue to cause poor ability to recognize identity. The purpose of our research work is to provide an approach that contributes to resolve face identification issues with large variations of parameters such as pose, illumination, and expression. For provable outcomes, we combined two algorithms: (a) robustness local binary pattern (LBP), used for facial feature extractions; (b) k-nearest neighbor (K-NN) for image classifications. Our experiment has been conducted on the CMU PIE (Carnegie Mellon University Pose, Illumination, and Expression) face database and the LFW (Labeled Faces in the Wild) dataset. The proposed identification system shows higher performance, and also provides successful face similarity measures focus on feature extractions.

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Section: Prior Workmentioning

confidence: 99%

Section: Prior Workmentioning

confidence: 99%

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Enhancing Face Identification Using Local Binary Patterns and K-Nearest Neighbors

Beli¹,

Guo

2017

J. Imaging

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“…CCTV sequences or online movies. Indeed, on the one hand, the different variations in facial micro/macro expressions [100], which generate fast, facial dynamics and the different processes such as ageing, which is an extremely slow, dynamic problem since the face evolves over large periods of time [18], have all an impact on AFR techniques. On the other hand, face acquisition in videos intrinsically creates facial dynamics due to camera motion, change of point of view, as well as head's movements or pose variations.…”

Section: Dynamic Facementioning

confidence: 99%

Automated Face Recognition: Challenges and Solutions

Olszewska¹

2016

Pattern Recognition - Analysis and Applications

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Automated face recognition (AFR) aims to identify people in images or videos using pattern recognition techniques. Automated face recognition is widely used in applications ranging from social media to advanced authentication systems. Whilst techniques for face recognition are well established, the automatic recognition of faces captured by digital cameras in unconstrained, real-world environment is still very challenging, since it involves important variations in both acquisition conditions as well as in facial expressions and in pose changes. Thus, this chapter introduces the topic of computer automated face recognition in light of the main challenges in that research field and the developed solutions and applications based on image processing and artificial intelligence methods.

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“…In this paper, we use local binary pattern (LBP) on histopathological images for prognostic purposes in urothelial carcinoma without segmenting individual cells. LBP have previously been applied with promising results on segmentation of histopathological images [6], other medical applications [9], [10], as well as other applications [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Prognostic prediction of histopathological images by local binary patterns and RUSBoost

Urdal

Engan

Kvikstad

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-A high recurrence rate, and progression to higher stages are observed for patients diagnosed with urothelial carcinoma (previously known as transitional cell carcinoma). Low prognostic value of the current grading systems result in extensive follow-up of patients for multiple years after first diagnosis. Although, the aid of computer systems for prognosis prediction of superficial urothelial carcinomas have been proposed, earlier analyses have been focused on using morphological features of cells and attributes describing the patient. In this study, we propose a system to aid in the prediction of prognostic information based on a texture analysis of histopathological images of superficial urothelial carcinoma. The analyses are conducted using the local binary pattern (LBP) and local variance (VAR) operators followed by a RUSBoost classifier. A dataset of 42 patients, consisting of 13 patients without recurrence, 14 with recurrence but not progression and 15 patients with progression are studied. Using a leave-one-out cross-validation, an accuracy of 70% and sensitivity of 84% is achieved.

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Analysis of Local Binary Patterns for face recognition under varying facial expressions

Cited by 11 publications

References 7 publications

Enhancing Face Identification Using Local Binary Patterns and K-Nearest Neighbors

Enhancing Face Identification Using Local Binary Patterns and K-Nearest Neighbors

Automated Face Recognition: Challenges and Solutions

Prognostic prediction of histopathological images by local binary patterns and RUSBoost

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