FLSNet: Robust Facial Landmark Semantic Segmentation

Kim, Hyeonwoo; Rew, Jehyeok; Hwang, Eenjun

doi:10.1109/access.2020.3004359

Cited by 15 publications

(6 citation statements)

References 36 publications

(43 reference statements)

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“… Kim et al (2020) implemented semantic segmentation to accurately extract facial landmarks. Semantic segmentation architecture and datasets containing facial images and ground truth pairs are introduced first.…”

Section: Methodsmentioning

confidence: 99%

Adjusting eye aspect ratio for strong eye blink detection based on facial landmarks

Dewi

Chen

Jiang

et al. 2022

PeerJ Computer Science

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Blink detection is an important technique in a variety of settings, including facial movement analysis and signal processing. However, automatic blink detection is very challenging because of the blink rate. This research work proposed a real-time method for detecting eye blinks in a video series. Automatic facial landmarks detectors are trained on a real-world dataset and demonstrate exceptional resilience to a wide range of environmental factors, including lighting conditions, face emotions, and head position. For each video frame, the proposed method calculates the facial landmark locations and extracts the vertical distance between the eyelids using the facial landmark positions. Our results show that the recognizable landmarks are sufficiently accurate to determine the degree of eye-opening and closing consistently. The proposed algorithm estimates the facial landmark positions, extracts a single scalar quantity by using Modified Eye Aspect Ratio (Modified EAR) and characterizing the eye closeness in each frame. Finally, blinks are detected by the Modified EAR threshold value and detecting eye blinks as a pattern of EAR values in a short temporal window. According to the results from a typical data set, it is seen that the suggested approach is more efficient than the state-of-the-art technique.

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Section: Methodsmentioning

confidence: 99%

Adjusting eye aspect ratio for strong eye blink detection based on facial landmarks

Dewi

Chen

Jiang

et al. 2022

PeerJ Computer Science

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“… Kim, H., et al [24], introduced a robust facial landmark semantic segmentation (FLSNet) based on dividing images into pixel units. The technique used datasets made up of pairs of facial images and ground truth data as well as a semantic segmentation architecture for indepth landmark detection.…”

Section: Literature Surveymentioning

confidence: 99%

A Color Facial Image Segmentation using Bit Plane Slicing and Block Truncation Coding Techniques

Al-Obaidi,

Al-Khafaji

2024

Iraqi Journal of Science

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Facial image segmentation is vital and complementary in facial compression systems. IT generally represents the first step before using any coding techniques, but mostly utilizes traditional segmentation techniques of grayscale image base due to their simplicity and popularity. This paper introduces a new facial segmented image of the color base that exploits the spectral and spatial image information using differencing coding, and compression techniques of Bit Plane Slicing (BPS) and Block Truncation Coding (BTC), respectively. The tested results show that this technique is fast as it achieved about 0.3619 second on average and efficiently segmented the facial image automatically to nearly quarter the original image size.

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“…DCNNs carry out the under-lying layout of the entire facial image. For instance, Kim et al [20] addressed face parsing using facial landmark data in conjunction with DCNNs. According to Mahta et al [21], DCNNs can significantly lower face parsing's computational cost, making a network suitable for real-time applications.…”

Section: B Face Parsingmentioning

confidence: 99%

BADANet: Boundary Aware Dilated Attention Network for Face Parsing

Raghavendra,

Abhilash,

Nookala

et al. 2023

IEEE Access

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Over the past few years, deep learning techniques have revolutionized the field of face parsing by utilizing massive datasets to generate high-level features and achieve outstanding performance. Usually, these techniques involve Convolutional Neural Networks (CNNs) to derive features from the input image and then a decoder network to forecast the semantic labels for every pixel. Even with complex deep convolutional neural networks (DCNN), incorrect parsing leads to a semantic gap between identical features, especially at boundary levels. Face parsing has encountered additional challenges due to the inclusion of pose, illumination and facial expressions. In order to address these issues, a Boundary Aware Dilated Attention Network (BADANet) is introduced which explores the use of multiscale techniques to improve the accuracy and robustness of the per-pixel frames. BADANet's dilated attention module has been combined with a lightweight backbone to achieve exceptional results on LaPa, CelebAMask-HQ, and iBugMask. Extensive evaluations of the proposed method demonstrate its performance to be on par with various state-of-the-art techniques. The pipeline for the BADANet is available at https://github.com/abhigoku10/BADANet

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FLSNet: Robust Facial Landmark Semantic Segmentation

Cited by 15 publications

References 36 publications

Adjusting eye aspect ratio for strong eye blink detection based on facial landmarks

Adjusting eye aspect ratio for strong eye blink detection based on facial landmarks

A Color Facial Image Segmentation using Bit Plane Slicing and Block Truncation Coding Techniques

BADANet: Boundary Aware Dilated Attention Network for Face Parsing

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