Facial descriptors for human interaction recognition in still images

Tanisik, Gokhan; Zalluhoglu, Cemil; Ikizler-Cinbis, Nazli

doi:10.1016/j.patrec.2016.01.002

Cited by 25 publications

(13 citation statements)

References 21 publications

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“…Examples include localization of agents' body pose (e.g. [53]), or face pose [54], such as by deep CNN features, and features based on distance between agents [52,53]. Kong & Fu [55] have further used the localization of body components and their relations for the recognition of social interactions, by modelling a set of spatio-temporal relations between body parts.…”

Section: Visual Understanding Of Social Interactions In Previous Workmentioning

confidence: 99%

Image interpretation above and below the object level

Ben-Yosef

Ullman

2018

Interface Focus.

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Computational models of vision have advanced in recent years at a rapid rate, rivalling in some areas human-level performance. Much of the progress to date has focused on analysing the visual scene at the object level-the recognition and localization of objects in the scene. Human understanding of images reaches a richer and deeper image understanding both 'below' the object level, such as identifying and localizing object parts and sub-parts, as well as 'above' the object level, such as identifying object relations, and agents with their actions and interactions. In both cases, understanding depends on recovering meaningful structures in the image, and their components, properties and inter-relations, a process referred here as 'image interpretation'. In this paper, we describe recent directions, based on human and computer vision studies, towards human-like image interpretation, beyond the reach of current schemes, both below the object level, as well as some aspects of image interpretation at the level of meaningful configurations beyond the recognition of individual objects, and in particular, interactions between two people in close contact. In both cases the recognition process depends on the detailed interpretation of so-called 'minimal images', and at both levels recognition depends on combining 'bottom-up' processing, proceeding from low to higher levels of a processing hierarchy, together with 'top-down' processing, proceeding from high to lower levels stages of visual analysis.

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Section: Visual Understanding Of Social Interactions In Previous Workmentioning

confidence: 99%

Image interpretation above and below the object level

Ben-Yosef

Ullman

2018

Interface Focus.

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“…Recently, deep learning (DL) [14] has achieved a great success in terms of computer vision and pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Recognition Using Kernel Entropy Component Analysis Network and DAGSVM

Chen

et al. 2021

Complexity

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Facial expression recognition (FER) plays a significant part in artificial intelligence and computer vision. However, most of facial expression recognition methods have not obtained satisfactory results based on low-level features. The existed methods used in facial expression recognition encountered the major issues of linear inseparability, large computational burden, and data redundancy. To obtain satisfactory results, we propose an innovative deep learning (DL) model using the kernel entropy component analysis network (KECANet) and directed acyclic graph support vector machine (DAGSVM). We use the KECANet in the feature extraction stage. In the stage of output, binary hashing and blockwise histograms are adopted. We sent the final output features to the DAGSVM classifier for expression recognition. We test the performance of our proposed method on three databases of CK+, JAFFE, and CMU Multi-PIE. According to the experiment results, the proposed method can learn high-level features and provide more recognition information in the stage of training, obtaining a higher recognition rate.

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“…Understanding text in action video images is a hot topic for researchers in the fields of computer vision and image processing [1,2,3]. This is because of its several real world applications such as surveillance, human-computer interaction, robotics and indexing videos [2,3]. There are powerful methods for recognizing the action in the video proposed in literature [2,3].…”

Section: Introductionmentioning

confidence: 99%

A New DCT-FFT Fusion Based Method for Caption and Scene Text Classification in Action Video Images

Nandanwar

Shivakumara

Manna³

et al. 2020

Pattern Recognition and Artificial Intelligence

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Achieving better recognition rate for text in video action images is challenging due to multi-type texts with unpredictable backgrounds. We propose a new method for the classification of captions (which is edited text) and scene texts (which is part of an image in video images of Yoga, Concert, Teleshopping, Craft, and Recipe classes). The proposed method introduces a new fusion criterion-based on DCT and Fourier coefficients to extract features that represent good clarity and visibility of captions to separate them from scene texts. The variances for coefficients of corresponding pixels of DCT and Fourier images are computed to derive the respective weights. The weights and coefficients are further used to generate a fused image. Furthermore, the proposed method estimates sparsity in Canny edge image of each fused image to derive rules for classifying caption and scene texts. Lastly, the proposed method is evaluated on images of five above-mentioned action image classes to validate the derived rules. Comparative studies with the state-of-the-art methods on the standard databases show that the proposed method outperforms the existing methods in terms of classification. The recognition experiments before and after classification show that the recognition performance rate improves significantly after classification.

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Facial descriptors for human interaction recognition in still images

Cited by 25 publications

References 21 publications

Image interpretation above and below the object level

Image interpretation above and below the object level

Facial Expression Recognition Using Kernel Entropy Component Analysis Network and DAGSVM

A New DCT-FFT Fusion Based Method for Caption and Scene Text Classification in Action Video Images

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