Towards on Develop a Framework for the Evaluation and Benchmarking of Skin Detectors Based on Artificial Intelligent Models Using Multi-Criteria Decision-Making Techniques

Evaluation and benchmarking of skin detectors are challenging tasks because of multiple evaluation attributes and conflicting criteria. Although several evaluating and benchmarking techniques have been proposed, these approaches have many limitations. Fixing several attributes based on multi-attribute benchmarking approaches is particularly limited to reliable skin detection. Thus, this study aims to develop a new framework for evaluating and benchmarking skin detection on the basis of artificial intelligent models using multi-criteria analysis. For this purpose, two experiments are conducted. The first experiment consists of two stages: (1) discussing the development of a skin detector using multi-agent learning based on different color spaces to create a dataset of various color space samples for benchmarking and (2) discussing the evaluation and testing the developed skin detector according to multi-evaluation criteria (i.e. reliability, time complexity, and error rate within dataset) to create a decision matrix. The second experiment applies different decision-making techniques (AHP/SAW, AHP/MEW, AHP/HAW, AHP/TOPSIS, AHP/WSM, and AHP/WPM) to benchmark the results of the first experiment (i.e. the developed skin detector). Then, we discuss the use of the mean, standard deviation, and paired sample [Formula: see text]-test to measure the correlations among the different techniques based on ranking results.

A connectionist computational method for face recognition

In this work, a modified version of the elastic bunch graph matching (EBGM) algorithm for face recognition is introduced. First, faces are detected by using a fuzzy skin detector based on the RGB color space. Then, the fiducial points for the facial graph are extracted automatically by adjusting a grid of points to the result of an edge detector. After that, the position of the nodes, their relation with their neighbors and their Gabor jets are calculated in order to obtain the feature vector defining each face. A self-organizing map (SOM) framework is shown afterwards. Thus, the calculation of the winning neuron and the recognition process are performed by using a similarity function that takes into account both the geometric and texture information of the facial graph. The set of experiments carried out for our SOM-EBGM method shows the accuracy of our proposal when compared with other state-of the-art methods.

LOCALIZATION AND RECOGNITION OF DYNAMIC HAND GESTURES BASED ON HIERARCHY OF MANIFOLD CLASSIFIERS

Generally, the dynamic hand gestures are captured in continuous video sequences, and a gesture recognition system ought to extract the robust features automatically. This task involves the highly challenging spatio-temporal variations of dynamic hand gestures. The proposed method is based on two-level manifold classifiers including the trajectory classifiers in any time instants and the posture classifiers of sub-gestures in selected time instants. The trajectory classifiers contain skin detector, normalized skeleton representation of one or two hands, and motion history representing by motion vectors normalized through predetermined directions (8 and 16 in our case). Each dynamic gesture is separated into a set of sub-gestures in order to predict a trajectory and remove those samples of gestures, which do not satisfy to current trajectory. The posture classifiers involve the normalized skeleton representation of palm and fingers and relative finger positions using fingertips. The min-max criterion is used for trajectory recognition, and the decision tree technique was applied for posture recognition of sub-gestures. For experiments, a dataset “Multi-modal Gesture Recognition Challenge 2013: Dataset and Results” including 393 dynamic hand-gestures was chosen. The proposed method yielded 84–91% recognition accuracy, in average, for restricted set of dynamic gestures.

A FOUR-PHASES METHODOLOGY TO PROPOSE ANTI-PORNOGRAPHY SYSTEM BASED ON NEURAL AND BAYESIAN METHODS OF ARTIFICIAL INTELLIGENCE

Pornographic images are disturbing and malicious contents that are easily available through Internet technology. It has a negative and lasting effect on children who use the Internet; thus, pornography has become a serious threat not only to Internet users but also to society at large. Therefore, developing efficient and reliable tools to automatically filter pornographic contents is imperative. However, the effective interception of pornography remains a challenging issue. In this paper, a four-phase anti-pornography system based on the neural and Bayesian methods of artificial intelligence is proposed. Primitive information on pornography is examined and then used to determine if a given image falls under the pornography category. First, we present a detailed description of preliminary study phase followed by the modeling phase for the proposed skin detector. An anti-pornography system is created in the development phase, which also includes the proposed pornography classifier based on skin detection. Finally, the performance assessment method for the proposed anti-pornography system is discussed in the evaluation phase.

CO2 Laser Treatment for the Second-Degree Scald of Mouse Skin

Nowadays CO2 laser has been regarded as the effective treatment methods of different kinds of cosmetology and dermatologic surgery. The second-degree scald is of frequent occurrence in people, but many methods on treat the second-degree scald are not good enough. Therefore, the process of second degree scald skin irradiated by the CO2 laser was monitored in the study. The second-degree scald of mice models were divided into two groups in order to contrastively observe the effect of CO2 laser on treating scald. Skin detector associating with the histopathologic examination were used to observe the changes of skin texture and pathologic morphology structure. Meanwhile the healing time was recorded about the two groups. The texture of skin surface irradiated CO2 laser was more glossy and distinct after healing, and the healing time was much faster as well. It was good agreement between skin detector images and histopathological architecture. The result suggests the CO2 laser can significantly cure the the second-degree scald.