Positional Ternary Pattern (PTP): An edge based image descriptor for human age recognition

Iqbal, Tauhid Bin; Ryu, Byungyong; Song, Gihun

doi:10.1109/icce.2016.7430617

Cited by 16 publications

(11 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since we analyze the positional relationship among the top k Kirsch edge responses to extract further information, it is very important to choose the number of k carefully. Existing descriptors such as LDP use three edge responses [11]; however, the top two edge responses yield consistent performances in recent works owing to their efficacy in characterizing the local texture structure [14,15,21]. Motivated by this, we utilize the positional information of the top two edge responses ( P 1 and P 2 ) and analyze their relation to extract further detail of the local structure of the crucial texture-primitives, such as edge and corner.…”

Section: Ldsp Coding Schemementioning

confidence: 99%

“…After computing the LDSP code at each pixel of an image, a histogram of the code-bins of that image can be regarded as the feature vector. However, to gather more spatial information, typically the image is divided into different spatial regions, and histograms of all these regions are concatenated to generate the final feature vector [2,9,11,14]. For this purpose, we divide the facial image into several nonoverlapping x × y sized uniform blocks, followed by generating a histogram for each block.…”

Section: Feature-vector Generationmentioning

confidence: 99%

“…Besides, edge-based methods use the edge responses from compass masks [12] and pick top k-directional responses to formulate the feature codes. Examples of some of the prominent edge-based descriptors may include the local directional number pattern (LDN) [9], local directional pattern (LDP) [11], local principal texture pattern (LPTP) [13], and positional ternary pattern (PTP) [14,15]. Although these descriptors exhibit promising results so far, achieving stable and robust definition of the structural detail of the local texture is still an issue for them.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Local directional-structural pattern for person-independent facial expression recognition

Makhmudkhujaev¹,

Iqbal²,

Ryu³

et al. 2019

Turk J Elec Eng & Comp Sci

Self Cite

View full text Add to dashboard Cite

Existing popular descriptors for facial expression recognition often suffer from inconsistent feature description, experiencing poor accuracies. We present a new local descriptor, local directional-structural pattern (LDSP), in this work to address this issue. Unlike the existing local descriptors using only the texture or edge information to represent the local structure of a pixel, the proposed LDSP utilizes the positional relationship of the top edge responses of the target pixel to extract more detailed structural information of the local texture. We further exploit such information to characterize expression-affiliated crucial textures while discarding the random noisy patterns. Moreover, we introduce a globally adaptive thresholding strategy to exclude futile flat patterns. Hence, LDSP offers a stable description of facial expressions with the explicit representation of the expression-affiliated features along with the exclusion of random futile textures. We visualize the efficacy of the proposed method in three folds. First, the LDSP descriptor possesses a moderate code-length owing to the exclusion of the futile patterns, yielding less computation time than other edge descriptors. Second, for person-independent expression recognition in benchmark datasets, LDSP demonstrates higher accuracy than existing descriptors and other state-of-the-art methods. Third, LDSP shows better performance than other descriptors against noise and low resolution, exhibiting its robustness under such uneven conditions.

show abstract

Section: Ldsp Coding Schemementioning

confidence: 99%

Section: Feature-vector Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Local directional-structural pattern for person-independent facial expression recognition

Makhmudkhujaev¹,

Iqbal²,

Ryu³

et al. 2019

Turk J Elec Eng & Comp Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…LBP is advantageous in monotonic illumination but shows sensitivity towards the noise and non-monotonic illuminations [29]- [30].3 Moreover, because of this naive texture specific coding scheme, LBP code is often inconsistent in capturing the direction of the edge appropriately [27]- [28], which, in turn, makes the LBP code weak in wrinkle patterns, where the edges are most prominent. Conversely, edge-based descriptors such as Positional Ternary Pattern (PTP) [27], Local Directional Pattern (LDP) [29] and Local Directional Pattern (LDN) [30] use the position of top few responses of compass masks to denote the principal axes of the edge, and generate their codes using such edge-directional information. Although edge-based descriptors show their efficacy in preserving the principal direction of wrinkle (e.g., edge) properly, they fail short to preserve many other variations.…”

Section: A Related Workmentioning

confidence: 99%

Mining Wrinkle-Patterns with Local EdgePrototypic Pattern (LEPP) Descriptor for the Recognition of Human Age-groups

Iqbal¹,

Chae²

2018

IJIGSP

Self Cite

View full text Add to dashboard Cite

Human age recognition from face image relies highly on a reasonable aging description. Considering the disparate and complex face-aging variation of each person, aging description needs to be defined carefully with detailed local information. However, aging description relies highly on the appropriate definition of different aging-affiliated textures. Wrinkles are considered as the most discernible textures in this regard owing to their significant visual appearance in human aging. Most of the existing image-descriptors, however, fail short to preserve diverse variations of wrinkles, such as a) characterizing stronger and smoother wrinkles, appropriately, b) distinguishing wrinkles from nonwrinkle patterns, and c) characterizing the proper texturestructures of the pixels belonging to the same wrinkle. In this paper, we address these issues by presenting a new local descriptor, Local Edge-Prototypic Pattern (LEPP) with the notion that LEPP preserves different variations of wrinkle-patterns appropriately in representing the aging description. In the coding, LEPP sets prototypic restrictions for each neighboring pixel using their relation with center pixel when they belong to an inlying-edge, and utilize such restrictions, afterwards, to prioritize specific neighbors showing significant edge-signature. This strategy appropriately encodes the inlying edge structure of aging-affiliated textures and simultaneously, avoids featureless texture. We visualize the stability of LEPP in terms of its robustness under noise. Our experiments show that LEPP preserves discernible aging variations yielding better accuracies than the state-of-theart methods in popular age-group datasets.

show abstract

“…To overcome face ageing issue in AFR, methods need to take properly into account facial ageing patterns [18]. Indeed, over time, not only face characteristics such as its shape or lines are modified [19], but other aspects are changing as well, e.g. hairstyle [20].…”

Section: Ageing Of the Facementioning

confidence: 99%

Automated Face Recognition: Challenges and Solutions

Olszewska¹

2016

Pattern Recognition - Analysis and Applications

View full text Add to dashboard Cite

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.

show abstract

Positional Ternary Pattern (PTP): An edge based image descriptor for human age recognition

Cited by 16 publications

References 12 publications

Local directional-structural pattern for person-independent facial expression recognition

Local directional-structural pattern for person-independent facial expression recognition

Mining Wrinkle-Patterns with Local EdgePrototypic Pattern (LEPP) Descriptor for the Recognition of Human Age-groups

Automated Face Recognition: Challenges and Solutions

Contact Info

Product

Resources

About