Any kind of compassionate thoughts can't be expressed through words, but it appears on their facial expression. So, the facial expression reveals the emotions of individuals. The recognition of such emotions can be understood correctly or sometimes ambiguously from the opponent. Hence, there is a scope for automatic facial expression recognition (FER) in the context of image processing. The FER system has three different phases: face detection, feature extraction and expression classifi-cation. In face detection phase, Viola Jones face detector is used to crop the original image such that only the face region is retained by removing the unwanted region. In feature extraction stage, High-order Joint Derivative Lo-cal Binary Pattern (HJDLBP) and Local Binary Pattern (LBP) histogram algorithms are used for extracting fea-tures from the cropped image. In last stage, Support Vec-tor machine (SVM) classifier is used in finding the precise facial expression.CK+ dataset has been used for training and testing, which consist of 442 image samples. We have considered six different universal possible ex-pressions such as, happy, anger, disgust, fear, surprised, and sad for identification. The experimental results indi-cate that the overall accuracy of the proposed system was 74.8%, which is high compare to the results available in literature.

The infotainment systems are acquiring wide popularity in automotive domain. These systems are manually operated and require physical contact for interaction. In the present scenario the consumers are demanding a smart phone like experience from the vehicle’s console unit. Thus, there is a wide scope for enhancing the mode of interaction and introducing a touch less interface system. The gesture interface approach is a new possibility in this domain. In this method the skin detection plays an important role in segmenting hand region. There are various approaches for hand detection based on skin region identification. The fundamental challenge in skin detection lies in various factors such as illumination, background, camera characteristics, and ethnicity. The gesture detection in automotive environment is further challenging task due to significant impact of wide variation in light, continuous changing background and hindrance caused by vehicle movement. In the present work, design of hand gesture interface for rear seat passenger is discussed. The interface is developed to interact with media player application of infotainment system based on efficient skin detection technique. The objectives of work include study of various skin color modeling, analysis of combination of color spaces, study of hand feature extraction and recognition techniques, design of lab setup for experimentation, implementing gesture interface to access media player application of an infotainment system. The developed prototype lab set up is used for analyzing the skin classifiers and designing a Hi-Vi skin classifier. Further, a user friendly interface is developed using Hi-Vi algorithm with multimode interface features. The evaluation of developed system shows high TPR and low FPR. 

Breast cancer is one of the leading causes of death in women all over the world. Computer based diagnosis system assists radiologist in the effective treatment of breast cancer. To design an efficient classification system for masses in digital mammograms, we have to use efficient algorithms for feature selection to reduce the feature space of mammogram classification problem. The proposed study explores the use of Firefly algorithm to select a subset of features. Artificial neural network and support vector machine classifiers are employed to evaluate fitness of the selected features. Features selected by Firefly algorithm are used to classify masses into benign and malignant, using artificial neural network and support vector machine classifiers. The proposed method employed over 651 mammograms obtained from the Database of Digitized Screen-film Mammograms. Classification results show that Firefly algorithm with artificial neural network is superior to Firefly algorithm with support vector machine. Artificial neural network achieves accuracy of 95.23% with 94.43% sensitivity, 93.94% specificity and area under curve Az=0.965±0.008. On the other hand, support vector machine classifier achieves an accuracy of 92.47% with 96.14% sensitivity, 88.53% specificity and area under curve Az=0.951±0.009.Results obtained with Firefly algorithm shows that it will be useful for effective treatment of breast cancer.

The impact of Accident effect, a lot of human livelihoods and their career, due to the negligence of wearing the helmets on a daily basis in India. To end this suffering, we have a social responsibility development projects. The motorcycle driver without wearing the helmet can be killed if it hit or crash and bring his life in danger. Wearing a helmet reduces the possibility of danger of life. In India, for example, wearing a helmet is mandatory in the law and the drivers need to follow it. Similar rules are in many countries. Therefore, this project is designed to improve motorcycle safety and make motorcyclist compulsory for wearing it. As you can see, we have a lot to think about it. It is a type that is used in the driver's helmet to drive a bike safely. The aim of this paper is to protect the driver life and decrease the death rate at road accident by making the helmet. It uses a hands-free kit and advances feature like compulsory of wearing helmet and theft security. Bluetooth wireless communication module (2.4 GHz band transceiver) can be used for transmission between the transmitter and the receiver. The driver needs to wear a helmet otherwise a helmet automatically keeps the engine OFF. If rider wears the helmet, ignition will be automatically ON. In the sensor, we transmit information to the wireless communication module, which is connected to the bike. This system is one of the devices built in the helmet; the second devices are inside the bike. Monitor recognizes the power devices with a Force-sensing resistor (FSR). Wireless data receiver, encoder, and a transmitter are used to communicate helmet with the bike. AT mega controller in both devices used as a CPU. It is one of the most advanced electronic projects for the Road Safety Systems.

One of the popular descriptor for texture classification is the local binary pattern (LBP). LBP and its variants derives local texture features effectively. This paper integrates the significant local features derived from uniform LBPs(ULBP) and threshold based conversion factor non-uniform (NULBP) with complete textons. This integrated approach represents the complete local structural features of the image.   The ULBPs are proposed to overcome the wide histograms of LBP. The ULBP contains fundamental aspects of local features.  The LBP is more prone to noise and this may transform ULBP into NULBP and this degrades the overall classification rate. To addresses this, this paper initially transforms back, the ULBPs that are converted in to NULBPs due to noise using a threshold based conversion factor and derives noise resistant fundamental texture (NRFT) image. In the literature texton co-occurrence matrix(TCM) and multi texton histogram (MTH) are derived on a 2x2 window. The main disadvantage of the above texton groups is they fail in representing complete textons. In this paper we have integrated our earlier approach “complete texton matrix (CTM)” [16] on NRFT images. This paper computes the gray level co-occurrence matrix (GLCM) features on the proposed NRFCTM (noise resistant fundamental complete texton matrix) and the features are given to machine learning classifiers for a precise classification. The proposed method is tested on the popular databases of texture classification and classification results are compared with existing methods.

The Traffic-Sign detection and recognition plays significant role in the design of autonomous driverless cars for navigation purpose as well as to assist a driver for alerting and educating him about the tracked signage on the road side. The main objective of this paper is to highlight an automatic process of detection of Region Of Interest (ROI) which marks or isolates signage’s from color video streams and performs classification of automatically detected signage’s based on support vector machine (SVM) classifiers trained over Local Binary Pattern (LBP) features. The training dataset was captured through 13 mega pixel mobile camera in different illumination and light conditions and due to randomness the data base complexity is very high. The robustness of the proposed system is measured on the bases its of capability of automatic detection and classification of ROI in a given video stream and backed with a comprehensive result analysis presented in this piece of work.

As known, the contrast is a highly important feature by which the visual quality of digital images can be judged as adequate or poor. Hence, many methods exist for contrast enhancement, where the complexity of those methods usually varies due to the utilization of different concepts. In this article, a simple yet efficient algorithm is introduced for contrast enhancement of digital images. The proposed algorithm consists of four distinct stages: In the first stage, the hyperbolic sine function is applied to provide a simple contrast modification. In the second stage, a modified power-law function is utilized to control the amount of contrast adjustment. In the third stage, the standard sigmoid function is used to remap the image pixels into an “S” shape, which can provide further contrast enhancement. In the final stage, a contrast stretching function is applied to remap the image pixels into their natural dynamic range. The performed computer experiments on different low-contrast images demonstrated the efficiency of the proposed algorithm in processing synthetic and real degraded images, as it provided better and clearer results when compared to several existing contrast enhancement algorithms. To end with, the proposed algorithm can be used as a contrast processing step in many image-related applications.