Rapid hand posture recognition using Adaptive Histogram Template of Skin and hand edge contour

With the rapid rising of living standard, people gradually developed higher shopping enthusiasm and increasing demand for garment. Nowadays, an increasing number of people pursue fashion. However, facing too many types of garment, consumers need to try them on repeatedly, which is somewhat time-and energy-consuming. Besides, it is difficult for merchants to master the real-time demand of consumers. Herein, there is not enough cohesiveness between consumer information and merchants. Thus, a novel fashion evaluation method on the basis of the appearance weak feature is proposed in this paper. First of all, image database is established and three aspects of appearance weak feature are put forward to characterize the fashion level. Furthermore, the appearance weak features are extracted according to the characters' facial feature localization method. Last but not least, consumers' fashion level can be classified through support vector product, and the classification is verified with the hierarchical analysis method. The experimental results show that consumers' fashion level can be accurately described based on the indexes of appearance weak feature and the approach has higher application value for the clothing recommendation system.

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“…The edge detection technique can be used to finger the gesture recognition [23]. People's wrist, finger edge, and neck edge are detected.…”

Section: The Accessories Index Includes Neck Andmentioning

confidence: 99%

Fashion Evaluation Method for Clothing Recommendation Based on Weak Appearance Feature

Zhang

Liu

Shi

et al. 2017

Scientific Programming

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“…Many pixel-based hand segmentation methods have been proposed in the literature [13,14,15,16,17,18,19,20,21,22,23,24]. The feature descriptor used to perform the detection is a critical factor.…”

Section: Hand Detectionmentioning

confidence: 99%

“…These techniques may be computationally prohibitive for real-time applications, a problem that is exacerbated by the limited computational resources of mobile and wearable devices. Researchers have also explored combining color with additional features such as texture [14,16,18,23]). In [14], a generic pixel-level hand detector based on a combination of color, texture, and gradient histogram features is trained using over 600 manually labeled hand images (over 200 million labeled pixels) acquired under various illumination conditions and backgrounds, and has shown to outperform several baseline approaches.…”

Section: Hand Detectionmentioning

confidence: 99%

On-the-fly hand detection training with application in egocentric action recognition

Kumar

Kyal

et al. 2015

2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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We propose a novel approach to segment hand regions in egocentric video that requires no manual labeling of training samples. The user wearing a head-mounted camera is prompted to perform a simple gesture during an initial calibration step. A combination of color and motion analysis that exploits knowledge of the expected gesture is applied on the calibration video frames to automatically label hand pixels in an unsupervised fashion. The hand pixels identified in this manner are used to train a statisticalmodel-based hand detector. Superpixel region growing is used to perform segmentation refinement and improve robustness to noise. Experiments show that our hand detection technique based on the proposed on-the-fly training approach significantly outperforms state-of-the-art techniques with respect to accuracy and robustness on a variety of challenging videos. This is due primarily to the fact that training samples are personalized to a specific user and environmental conditions. We also demonstrate the utility of our hand detection technique to inform an adaptive video sampling strategy that improves both computational speed and accuracy of egocentric action recognition algorithms. Finally, we offer an egocentric video dataset of an insulin self-injection procedure with action labels and hand masks that can serve towards future research on both hand detection and egocentric action recognition.

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“…Some skin detection algorithms described in [24], [25] and [26], which is a method for skin detection using a proposed skin histogram and a pre-learning step. Tofighi et all [1], described a method for hand detection that we used it to detect skin areas more accurate and robust to light change. We use normalized RGB of input image for training images.…”

Section: A Skin Detectionmentioning

confidence: 99%

“…Figure 3. Back Projection of skins using forehead histogram [1] As shown in figure 5, the brighter areas are more probable to be skin area (as they actually are), whereas the darker areas have less probability. Please note that these "dark" areas belong to surfaces that have some shadows.…”

Section: A Skin Detectionmentioning

confidence: 99%

Hand pointing detection using live histogram template of forehead skin

Tofighi

Afarin

Raahemifar

et al. 2014

2014 19th International Conference on Digital Signal Processing

Self Cite

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Abstract-Hand pointing detection has multiple applications in many fields such as virtual reality and control devices in smart homes. In this paper, we proposed a novel approach to detect pointing vector in 2D space of a room. After background subtraction, face and forehead is detected. In the second step, forehead skin H-S plane histograms in HSV space is calculated. By using these histogram templates of user's skin, and back projection method, skin areas are detected. The contours of hand are extracted using Freeman chain code algorithm. Next step is finding fingertips. Points in hand contour which are candidates for the fingertip can be found in convex defects of convex hull and contour. We introduced a novel method for finding the fingertip based on the special points on the contour and their relationships. Our approach detects hand-pointing vectors in live video from a common webcam with 94%TP and 85%TN.

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Rapid hand posture recognition using Adaptive Histogram Template of Skin and hand edge contour

Cited by 21 publications

References 17 publications

Fashion Evaluation Method for Clothing Recommendation Based on Weak Appearance Feature

Fashion Evaluation Method for Clothing Recommendation Based on Weak Appearance Feature

On-the-fly hand detection training with application in egocentric action recognition

Hand pointing detection using live histogram template of forehead skin

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