Human Face Detection and Tracking Using RetinaFace Network for Surveillance Systems

Wibowo, Moh Edi; Ashari, Ahmad; Subiantoro, Ardacandra; Wahyono, Wahyono

doi:10.1109/iecon48115.2021.9589577

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…We acknowledge the significance of this dataset in the field of VSR and aim to provide a comprehensive analysis of the techniques employed for feature extraction. Ma et al [63] utilized the RetinaFace tracker [64] to detect the faces present in the video frames. Following this, the authors utilized the Face Alignment Network (FAN) [65] for the detection of facial landmarks, which helps to accurately locate the lip area in each frame.…”

Section: Video Sampling Face Localization Roi Extractionmentioning

confidence: 99%

Deep Learning-Based Holistic Speaker Independent Visual Speech Recognition

Nemani¹,

Krishna²,

Ramisetty³

et al. 2023

IEEE Trans. Artif. Intell.

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Speaker-independent visual speech recognition (VSR) is a complex task that involves identifying spoken words or phrases from video recordings of a speaker's facial movements. Decoding the intricate visual dynamics of a speaker's mouth in a high-dimensional space is a significant challenge in this field. To address this challenge, researchers have employed advanced techniques that enable machines to recognize human speech through visual cues automatically. Over the years, there has been a considerable amount of research in the field of VSR involving different algorithms and datasets to evaluate system performance. These efforts have resulted in significant progress in developing effective VSR models, creating new opportunities for further research in this area. This survey provides a detailed examination of the progression of VSR over the past three decades, with a particular emphasis on the transition from speaker-dependent to speaker-independent systems. We also provide a comprehensive overview of the various datasets used in VSR research and the preprocessing techniques employed to achieve speaker independence. The survey covers the works published from 1990 to 2023, thoroughly analyzing each work and comparing them on various parameters. This survey provides an in-depth analysis of speaker-independent VSR systems evolution from 1990 to 2023. It outlines the development of VSR systems over time and highlights the need to develop end-to-end pipelines for speakerindependent VSR. The pictorial representation offers a clear and concise overview of the techniques used in speaker-independent VSR, thereby aiding in the comprehension and analysis of the various methodologies. The survey also highlights the strengths and limitations of each technique and provides insights into developing novel approaches for analyzing visual speech cues. Overall, This comprehensive review provides insights into the current state-of-the-art speaker-independent VSR and highlights potential areas for future research.

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Section: Video Sampling Face Localization Roi Extractionmentioning

confidence: 99%

Deep Learning-Based Holistic Speaker Independent Visual Speech Recognition

Nemani¹,

Krishna²,

Ramisetty³

et al. 2023

IEEE Trans. Artif. Intell.

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“…In the realms of image processing and pattern recognition, algorithms focused on local feature-based image matching are utilized for identifying specific objects or patterns in images. Local features are useful for identifying characteristics or patterns that exist in small parts of the image [1]. These algorithms target local features in an image, such as edges, corners, or textures, instead of analyzing the entire image [2].…”

Section: Introductionmentioning

confidence: 99%

Improved ORB Algorithm Through Feature Point Optimization and Gaussian Pyramid

Borman,

Harjoko,

2024

IJACSA

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Feature points obtained using traditional ORB methods often exhibit redundancy, uneven distribution, and lack scale invariance. This study enhances the traditional ORB algorithm by presenting an optimal technique for extracting feature points, thereby overcoming these challenges. Initially, the image is partitioned into several areas. The determination of the quantity of feature points to be extracted from each region takes into account both the overall number of feature points and the number of divisions that the image undergoes. This method tackles concerns related to the overlap and redundancy of feature points in the extraction process. To counteract the nonscale invariance issue in feature points obtained via the ORB method, a Gaussian pyramid is employed, and feature points are extracted at each level. Experimental findings demonstrate that our method successfully extracts feature points with greater uniformity and rationality, while preserving image matching accuracy. Specifically, our technique outperforms the traditional ORB algorithm by approximately 4% and the SURF algorithm by 2% in terms of matching performance. Additionally, the processing time of our proposed algorithm is three times faster than that of the SURF algorithm and twelve times faster than the SIFT algorithm.

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