A novel approach for Face Recognition using Local Binary Pattern

This paper presents Local Binary pattern (LBP) as an approach for face recognition with the use of some global features also. Face recognition has received quite a lot of attention from researchers in biometrics, pattern recognition, and computer vision communities. The idea behind using the LBP features is that the face images can be seen as composition of micro-patterns which are invariant with respect to monotonic grey scale transformations and robust to factors like ageing. Combining these micro-patterns, a global description of the face image is obtained. Efficiency and the simplicity of the proposed method allows for very fast feature extraction giving better accuracy than the other algorithms. The proposed method is tested and evaluated on ORL datasets combined with other university dataset to give a good recognition rate and 89% classification accuracy using LBP only and 98% when global features are combined with LBP. The method is also tested for real images to give good accuracy and recognition rate. The experimental results show that the method is valid and feasible

SAR Analysis Using a Dipole Antenna in a Non-layered and Multi-layered Human Head Model

The public complaints about health are on the rise as a result of mobile phone usage. Limits on the radiation strength emitted by these devices have already been recommended by standard bodies such as The Federal Communication Commission (FCC) of the United States and the International Commission on Non-Ionizing Radiation Protection (ICNIRP) to safeguard public from excessive exposure to electromagnetic fields. Some recent research have found that long-duration use of calls on mobile phones increases the incidence of health hazards and has negative consequences. After survey the research on investigation of specific absorption rate (SAR) in the human head is becoming increasingly essential. In proposed work, Investigations were done on how human head model and electromagnetic source interacted. The goal of this work is to demonstrate that the one layer head model is not a good model rather appear to be unreliable to evaluate SAR since genuine human skull tissue is not modelled in the same way. But investigation of SAR in six layers (Brain, CSF, Dura, Bone, Fat and Skin) human head appears better and reliable. Affection to the six layered human head may be dominant when exposed to electromagnetic (EM) fields

Personal Authentication Using Finger Images

As the need for personal authentication increases, biometrics systems have become the ideal answer to the security needs. This paper presents a novel personal authentication system which uses simultaneously acquired finger-vein and finger texture images of the same person. A virtual fingerprint is generated combining these two images. The result of the combination i.e. the virtual fingerprint is then subjected to pre-processing steps including binarization, normalization, enhancement and Region of Interest (ROI) segmentation. Gabor filter is used to extract features. The feature extracted image is matched with the database. This proposed system is designed such that to achieve better performance in terms of matching accuracy, execution time, memory required and security. DOI: 10.17762/ijritcc2321-8169.15017

Human Emotion Recognition using Electrocardiogram Signals

Human emotions are recognized using face recognition, speech recognition, physiological signals recognition etc. This paper represents Electrocardiogram (ECG) signal for emotion recognition thorough analysis of its psychological properties to recognize human emotion, it can reflect peoples true emotion and provide smooth interface between human and computer. Each signal is empirically decomposed by using Empirical Mode Decomposition (EMD) into finite set of small oscillatory activity called Intrinsic Mode Functions (IMF). The information components of interest are then combined to create feature v ector based on the combination methods for exploiting the fission - fusion processes provided by Hilbert - Huang transform. In the next stage, classification is performed by using Multi class Support Vector Machines to identify four emotional states (joy, ange r, sadness and pleasure) of human body. When we evaluated the algorithm on database recorded at university of Augsburg, the proposed method achieved improved recognition accuracy for subj ect - independent classification

Automatic Genre Classification Using Fractional Fourier Transform Based Mel Frequency Cepstral Coefficient and Timbral Features

This paper presents the Automatic Genre Classification of Indian Tamil Music and Western Music using Timbral and Fractional Fourier Transform (FrFT) based Mel Frequency Cepstral Coefficient (MFCC) features. The classifier model for the proposed system has been built using K-NN (K-Nearest Neighbours) and Support Vector Machine (SVM). In this work, the performance of various features extracted from music excerpts has been analysed, to identify the appropriate feature descriptors for the two major genres of Indian Tamil music, namely Classical music (Carnatic based devotional hymn compositions) & Folk music and for western genres of Rock and Classical music from the GTZAN dataset. The results for Tamil music have shown that the feature combination of Spectral Roll off, Spectral Flux, Spectral Skewness and Spectral Kurtosis, combined with Fractional MFCC features, outperforms all other feature combinations, to yield a higher classification accuracy of 96.05%, as compared to the accuracy of 84.21% with conventional MFCC. It has also been observed that the FrFT based MFCC effieciently classifies the two western genres of Rock and Classical music from the GTZAN dataset with a higher classification accuracy of 96.25% as compared to the classification accuracy of 80% with MFCC