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... addition to skin region detector in RGB, YC b C r detector is used. Taking advantages of more than one colour space gives additional accuracy (see Fig. 2). Skin-colour detector in YC b C r was built on the base of 25 face examples from different races (people of black skin-colour were not considered). enables the increase in accuracy. Usage of more then one colour space was already used (e.g. [Kuk03, Tan99]). ...
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... Humanless identification system requires some intelligent devices, uninterrupted power, and connectivity. Visitor identification is one of the solutions to this problem [1] [2]. The visitor identification system comes under the facial recognition system. ...
... This paper's researchers performed empirical experiments on two standard face recognition data sets. Kukharev proposed a real-time visitor identification model [1]. A three-stage face detection algorithm is suggested for face detection. ...
... Therefore, it is required to convert the RGB representation to an intensity free color space model. Hue-Saturation-Value (HSV) representation and YCrCb color space are the best options available [14]. In YCrCb, Y is the luminance component and CB and CR are the blue-difference and reddifference chroma components. ...
Sign language is the fundamental communication method among people who suffer from speech and hearing defects. The rest of the world doesn't have a clear idea of sign language. "Sign Language Communicator" (SLC) is designed to solve the language barrier between the sign language users and the rest of the world. The main objective of this research is to provide a low cost affordable method of sign language interpretation. This system will also be very useful to the sign language learners as they can practice the sign language. During the research available human computer interaction techniques in posture recognition was tested and evaluated. A series of image processing techniques with Hu-moment classification was identified as the best approach. To improve the accuracy of the system, a new approach height to width ratio filtration was implemented along with Hu-moments. System is able to recognize selected Sign Language signs with the accuracy of 84% without a controlled background with small light adjustments
... The skin detection algorithm is proposed to detect and extract the hand from its background. We refer to the model parameters proposed in [20], and redesign the experimental scene parameters in accordance with this study. The range of skin color is defined as: ...
Human-Computer interaction (HCI) with gesture recognition is designed to recognize a number of meaningful human expressions, and has become a valuable and intuitive computer input technique. Hand gestures are one of the most intuitive and common forms of communication, and can communicate a wide range of meaning. Vision-based hand gesture recognition has received a significant amount of research attention in recent years. However, the field still presents a number of challenges for researchers. In the vision-based hand gesture interaction process between humans and computers, gesture interpretation must be performed quickly and with high accuracy. In this paper, a low-cost HCI system with hand gesture recognition is proposed. This system uses several vision techniques. Skin and motion detection is used for capturing the region-of-interest from the background regions. A connected component labeling algorithm is proposed to identify the centroid of an object. To identify the exact area of hand gesture, the arm area is removed with the aid of a convex hull algorithm. Moreover, a real-time demonstration system is developed, based on a single-camera mechanism which allows for the use of wearable devices. Simulation results show that the recognition rate is still high, although some interference is encountered in the simulated environments.
... [4][5][6][7]. The background and hand can be segmented with the threshold values (T) as shown in the following eq2, ...
Hand Segmentation is the most important steps for hand gesture recognition.
If the correctly detect the hand, the hand gesture recognition accuracy will
increase. In our research, we used two different threshold-based segmentation
methods to detect the hand in various background conditions (simplex and
complex) and various illumination conditions and compare the accuracy. The
main objective of this paper is to investigate the best threshold values of CbCr
threshold-based method on the performance of hand segmentation process.
The proposed system used the CbCr colour threshold-based method with
various threshold values and otsu’s method for skin color hand segmentation.
... The motivation of our research is to find out the best threshold values of YCbCr colour space for human skin detection. YCbCr color space is the best colour model than HSV and RGB in colour clustering range and colour independent for skin segmentation [9,17,18,21]. According to the previous literature, YCbCr colour space is the best suitable colour space for human skin detection. ...
... The calculation process of YCbCr is very simple. YCbCr skin colour based segmentation is explained in [1,2,5,9,10].The YCbCr colour model is shown in Fig 2. In our experiment, firstly convert RGB to YCbCr by using the following equation (1). Fig.3 ...
The hand gesture recognition system is the hottest topic for the human-machine interaction and computer vision fields. The hand gesture recognition system is still a challenging research area in computer vision for human-computer interaction because of various device conditions, various illumination effects, and very complex background. The recognition of hand gestures used in various application areas: such as sign language recognition, man-machine interaction, human-robot interaction, and intelligent device control and many other application areas. The robust detection of hand in hand gesture recognition system has become a challenging task due to clutter background, dynamic background, and various illumination conditions in real-world conditions. Segmentation is the partitioning/separating the foreground hand region from the background region in an image. Segmentation is also pre-processing steps of the hand gesture recognition system. The recognition accuracy will increase if the hand region correctly detected. So, hand region detection is the main important step for the hand gesture recognition system.
... In order to evaluate our skin colour prediction model, we compared our skin colour model to the most known prediction skin colour models which proved their performance in face detection task (Kovac et al., 2003;Liposcak and Loncaric, 2000;Kukharev and Novosielski, 2004). This experimental study was performed on our test dataset and described in Table 3 which proves the effectiveness of the proposed skin model. ...
... Kovacetal (2003) 82.2% Liposcak and Loncaric (2000) 82.5% Kukharev and Novosielski (2004) 83.6% ...
... In order to evaluate our skin colour prediction model, we compared our skin colour model to the most known prediction skin colour models which proved their performance in face detection task (Kovac et al., 2003;Liposcak and Loncaric, 2000;Kukharev and Novosielski, 2004). This experimental study was performed on our test dataset and described in Table 3 which proves the effectiveness of the proposed skin model. ...
... Kovacetal (2003) 82.2% Liposcak and Loncaric (2000) 82.5% Kukharev and Novosielski (2004) 83.6% ...
... The human skin color for YCbCr color space was defined by several rules. Equation (5) comes from [13] and (6) comes from [16]. 76 < < 127 132 < < 173 (5) 85 < < 135 135 < < 180 > 80 ...
... the Multilayer Perceptrons (MLPs) and Probabilistic Neural Networks (PNNs) (Kukharev and Novosielski, 2004). The PNNs require a large amount of memory, because all the training data must be stored in the pattern layer. ...
Face detection is an important prior step for face recognition system which is widely used in security systems, face verification systems, telecommunication, video surveillance, facial expressions recognition, status authentication, etc. The proposed system is applied on many images which contain persons and extract the faces out of there automatically. The skin color, region-props and bounding-box are used as preprocessing tools for extracting regions. The Neural Networks (NNT) are used to recognize these regions are faces or not. This system has the ability to detect faces with various image conditions: Different poses and facial expirations, different and complex backgrounds and different luminance and lighting conditions. The system is tested on several color images. The detection rates for used databases are 67.6% for images which has background nonluminous, 88% for very dense images and 100% for non-dense images with a luminous background. © 2016 Asad Freihat, Radwan Abu-Gdairi, Hammad Khalil, Eman Abuteen, Mohammed Al-Smadi and Rahmat Ali Khan.
... Since YCbCr color space can better reflect the color clustering characteristics, so we convert the image into YCbCr space at first. Then, according to the theory presented by G.Kukharev, and A.Novosielsk [7], if Y, Cb, Cr values satisfy Y>80, 85<Cr<135,135<Cb<180, it can be considered belong to skin area. The result is as follows: ...
