List of Articles کریم فائز

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  1 - MHIDCA: Multi Level Hybrid Intrusion Detection and Continuous Authentication for MANET Security
  Soheila Mirzagholi Karim Faez
  Mobile ad-hoc networks have attracted a great deal of attentions over the past few years. Considering their applications, the security issue has a great significance in them. Security scheme utilization that includes prevention and detection has the worth of considerati More

  Mobile ad-hoc networks have attracted a great deal of attentions over the past few years. Considering their applications, the security issue has a great significance in them. Security scheme utilization that includes prevention and detection has the worth of consideration. In this paper, a method is presented that includes a multi-level security scheme to identify intrusion by sensors and authenticates using biosensors. Optimizing authentication and intrusion detection combination, we formulate the problem as a partially observable distributed stochastic system. In order to reduce the computation time, the parallel forward algorithm of Hidden Markov Model has been used. Due to the possibility of misdetection of the sensor and in order to increase the accuracy of observations, more than one sensor is selected in every step, the observations obtained from the sensors are combined for more accurate identification, and the system decides about the security status based on combined observations of the sensors. Bayesian theory has been used in sensors evidence fusion brought by increased accuracy and network security, which will be observed in the simulations. The use of this theory causes the increase of accuracy and security on networks. Manuscript profile
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  2 - Facial Expression Recognition Based on Structural Changes in Facial Skin
  Zeynab Shokoohi Karim Faez
  Facial expressions are the most powerful and direct means of presenting human emotions and feelings and offer a window into a persons’ state of mind. In recent years, the study of facial expression and recognition has gained prominence; as industry and services ar More

  Facial expressions are the most powerful and direct means of presenting human emotions and feelings and offer a window into a persons’ state of mind. In recent years, the study of facial expression and recognition has gained prominence; as industry and services are keen on expanding on the potential advantages of facial recognition technology. As machine vision and artificial intelligence advances, facial recognition has become more accessible and is now a key technique to be employed and used in creating more natural man-machine interactions, Computer vision, and health care. In this paper, we empirically evaluate facial representation based on statistical local features, Local Binary Patterns, for person-independent facial expression recognition. Different machine learning methods are systematically examined on several databases. Extensive experiments illustrate that LBP features are effective and efficient for facial expression recognition. In this paper, we proposed a face expression detection method based on the difference of a face expression andthe allocated special pattern to each expression. The analysis of the image detection system locally and through a sliding window (sliding) at multiple scales, are estimated. Multiple scales are extracted aslocally binary features. Through using the change point between windows, points of face are getting a directional movement. Through using points movement of whole facial expressions and rating system that is created thesuperfluouspoints are eliminated. The classifications are taken based on the nearest neighbor.To sum up this paper, the proposed algorithms are tested on Cohn-Kanade data set and the results showed the best performance and reliability into other algorithms. We investigated LBP features for the facial skin structural changes, which is seldom addressed in the existing literature. Manuscript profile
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  3 - Comparison of Different Linear Filter Design Methods for Handling Ocular Artifacts in Brain Computer Interface System
  Sahar Seifzadeh Karim Faez Mahmood Amiri
  Brain-computer interfaces (BCI) record brain signals, analyze and translate them into control commands which are relayed to output devices that carry out desired actions. These systems do not use normal neuromuscular output pathways. Actually, the principal goal of BCI More

  Brain-computer interfaces (BCI) record brain signals, analyze and translate them into control commands which are relayed to output devices that carry out desired actions. These systems do not use normal neuromuscular output pathways. Actually, the principal goal of BCI systems is to provide better life style for physically-challenged people which are suffered from cerebral palsy, amyotrophic lateral sclerosis, stroke, or spinal cord injury. One of the focal points in Brain-Computer Interface (BCI) systems is physiological artifacts handling. Physiological artifacts such as Electrooculography (EOG) and Electrooculography (EMG) are considered among the most important sources of physiological artifacts in BCI systems. Pre-processing is considerable step by means of next steps such as feature extraction and classification that we need clean signals without undesirable artifacts to have better classification rate. Using a linear filter to remove these artifacts is like a dime a dozen due to their acceptable results in recent BCI pre-processing researches. Although this method has different options, Forasmuch as the mu (8–13 Hz) and beta (16–25 Hz) frequency bands play a key role in classification of motor imagery we have decided to design two band pass filters with Elliptic and Butterworth Infinite impulse response designing methods in 8 to 40 Hz frequencies. Our results in Graz 2a dataset in BCI Competition IV indicates that, Elliptic band-pass filter has better performance for EOG removing in this specific dataset. Manuscript profile