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Human face detection has become an area of interest in various biometric applications such as crowd surveillance, human–computer interaction, and many security related areas. It is a major field of current research because there is no deterministic algorithm to find the face(s) in a given image. Face detection is challenging due to varying illumina...
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... This step involves face detection, registration, facial landmark identification, and face alignment. Some methods for detection of faces are in [27][28][29]. However, most researchers use open-source tools, including OpenCV, Dlib library, Haar cascade detector, and pre-trained functions in MATLAB. ...
This paper surveys some of the most important recent works related to micro-expression analysis. It includes discussions on algorithms for spotting and recognizing micro-expressions, their performances, databases, and new feature descriptor frameworks. Here, we use a reverse chronological order to discuss the state-of-the-art in a work-specific fashion. We provide a questionnaire-based comparative study of the literature and discuss future directions of micro-expression analysis. We expect this survey to expose micro-expressions to those interested in pursuing research in this emerging field.
... Yadav et al. [31] used color details of images to exploit skin, face, or eye color by applying a color convertor algorithm to remove background and other unnecessary details from images to detect/identify objects. Kalbkhani et al. [32] converted a RGB image [33,34] into YCBCR color using a nonlinear transformation and used an eye mapping algorithm based on a created face mask to detect the location of eyes on faces or the face itself in an image. ...
Recognizing facial expressions is a major challenge and will be required in the latest fields of research such as the industrial Internet of Things. Currently, the available methods are useful for detecting singular facial images, but they are very hard to extract. The main aim of face detection is to capture an image in real-time and search for the image in the available dataset. So, by using this biometric feature, one can recognize and verify the person’s image by their facial features. Many researchers have used Principal Component Analysis (PCA), Support Vector Machine (SVM), a combination of PCA and SVM, PCA with an Artificial Neural Network, and even the traditional PCA-SVM to improve face recognition. PCA-SVM is better than PCA-ANN as PCA-ANN has the limitation of a small dataset. As far as classification and generalization are concerned, SVM requires fewer parameters and generates less generalization errors than an ANN. In this paper, we propose a new framework, called FRS-DCT-SVM, that uses GA-RBF for face detection and optimization and the discrete cosine transform (DCT) to extract features. FRS-DCT-SVM using GA-RBF gives better results in terms of clustering time. The average accuracy received by FRS-DCT-SVM using GA-RBF is 98.346, which is better than that of PCA-SVM and SVM-DCT (86.668 and 96.098, respectively). In addition, a comparison is made based on the training, testing, and classification times.
... However, the traditional human skin color detection method [39,[49][50][51] has limitations because of skin color is subject to illumination condition and applicable to certain domains. Therefore, to overcome the limitations and to improve accuracy, a few hybrid skin color detection methods [51][52][53][54] combining two or more traditional methods have been reported in the literature. This section first investigates the strength and limitations of the different popular traditional skin color detection methods incorporating them into HFFD followed by investigation of the hybrid skin color detection methods. ...
... Yadav and Nain [53] investigated a more complicated hybrid skin color detection method. Skin color filtering formula of this method is: ...
... Outcomes of different methods are presented separately for each dataset in Table 2. Method 1 (RGB+ YCbCr) [51] is implemented in this study. Results of Method 2 (Red + HSL) [52] and Method 3 (RGB + YCbCr +HSV) [53] are the reported results in the papers where the sign '-' indicates no-availability of results. The performance of the methods on the individual datasets are discussed below. ...
... For face detection, [8] is proposed based on skin color segmentation and facial properties. An Algorithm is a competent analytical tool to evaluate different color patterns such as RGB, YcbCr, and HSV, as well as their skin color detection combinations. ...
For real-world applications, such as video monitoring, interaction between human machines and safety systems, face recognition is very critical. Deep learning approaches have demonstrated better results in terms of precision and processing speed in image recognition compared to conventional methods. In comparison to traditional methods. While facial detection problems with different commercial applications have been extensively studied for several decades, they still face problems with many specific scenarios, due to various problems such as severe facial occlusions, very low resolutions, intense lighting and exceptional changes in image or video compression artifacts, etc. The aim of this work is to robustly solve the issues listed above with a facial detection approach called Convolution Neural Network with Long short-term Model (CNN-mLSTM). This method first flattened the original frame, calculating the gradient image with Gaussian filter. The edge detection algorithm Canny-Kirsch Method will then be used to identify edge of the human face. The experimental findings suggest that the technique proposed exceeds the current modern methods of face detection.
... This research is conducted in the area of skin detection. Skin detection is currently used in various applications especially that involved identification of human skin and to differentiate between skin and non-skin using computer technology and sets of techniques and datasets [1]. Many researches have been done to come with the best techniques especially skin detector and specific skin detection methods that can accurately identify the skin and non-skin [2], and at the same time reduce the computational cost, increase the processing speed and better in performance [3]. ...
... Author in [137] employed combination of Skin based background removal. In the study which was carried out [1] , a localized approach for the detection of face according to segmentation of skin colour and facial characteristics were proposed. In the approach facial characteristics like bounding box, ratio of eccentricity for segmentation of face and eye-mouth hole detection were used. ...
This study review and analysis the literature on skin detector (SD), in order to establish the coherent taxonomy and figure out the gap on this pivotal research area. An extensive search is conducted to identify articles that deal with skin detection, skin segmentation, skin tone detector and skin recognition issues, related techniques are reviewed comprehensively and a coherent taxonomy of these articles is established. ScienceDirect, IEEE Xplore and Web of Science databases are checked for articles on skin detector. A total of 2803 papers are collected from 2007 to February 2018. The set comprised 173 articles. The largest portion of the papers (n=158/173) = 91% belong to Development and Design, that is aimed to develop an approach for skin classifier into skin and non-skin. A sum total of (n=5/173)=3% of the papers belong to Evaluation and Framework, (n=10/173) = 6% papers was categorized as Comparative Study. This study discusses the open challenges, motivations and recommendations of the related works. Furthermore, state-of-the-art is a step to demonstrate the novelty of the presented study by conducted a statistical analysis for previous studies such as (Dataset, Colour spaces, features, image type, and Classification techniques) as a future direction for other researchers who are interested in skin detector (SD).
... Finally, there are some hybrid methods that combine different approaches ( Chihaoui et al., 2016;Yadav and Nain, 2016;Zaidan et al., 2010 ). In particular, a method based on a Multilayer Perceptron Artificial Neural Network combined with a k-means clustering, which takes advantage of the color and texture information of the skin regions, has recently been presented in Al-Mohair et al. (2015) . ...
This paper presents a novel rule-based skin detection method that works in the YCbCr color space. The method is based on correlation rules that evaluate the combinations of chrominance values to identify the skin pixels in the YCb and YCr subspaces. The correlation rules depend on the shape and size of dynamically generated skin color clusters, which are computed on a statistical basis in the YCb and YCr subspaces for each single image, and represent the areas that include most of the candidate skin pixels. Comparisons with six well-known rule-based methods in literature carried out on four publicly available databases show that the proposed method outperforms the others in terms of quantitative performance evaluation parameters. Moreover, the qualitative analysis shows that the method achieves satisfactory results also in critical scenarios, including severe variations in illumination conditions.
... The proposed approach inclined by the fact that in extremely densed images of crowds, no single method or feature is appropriate to provide an accurate detection count of people due to few pixels per target, severe occlusion, clutter background and perspective effects. In fact the state-of-the-art human, head, or face detectors [7] achieve very low accuracy in such type of crowded scenes. It had been noticed that densely packed individuals could be behaved like irregular and in-homogeneous texture at a coarse scale and regular texture at a finer scale. ...
... These are tested on BAO multiple faces database within different orientation conditions. In [26] and [27], they used skin color segmentation with the facial feature for face detection. In their approach, visibility of facial feature like eye, nose, lips are required to measure the eccentricity which is used to know the probability of a skin color area being a face region. ...
Skin detection plays a vital role in various humanrelated computer vision applications, including human-computer interaction, medical diagnostic tools, and web content filtering. However, accurate skin detection remains challenging due to different factors such as luminosity variations, complex backgrounds, and diversity in skin tones. In this paper we present a rule-based skin detection method that applies dimensionality reduction using Principal Component Analysis (PCA) on pixels represented by multiple color channels. This process retains only the most pertinent information in form of principal components. Subsequently, skin detection is achieved according to the individual contribution of the pixels along these principal components. To evaluate the effectiveness of our approach, we conducted comprehensive experiments on the SFA dataset. Our method demonstrated consistently superior skin detection performance compared to other rule-based methods, in both quantitative and qualitative aspects across diverse scenarios.
