Face Identification is an important research topic in the field of computer vision and pattern recognition and has become a very active research area in recent decades. Recently multiwavelet-based neural networks (multiwavenets) have been used for function approximation and recognition, but to our best knowledge it has not been used for face Identification. This paper presents a novel approach for the Identification of human faces using Back-Propagation Adaptive Multiwavenet. The proposed multiwavenet has a structure similar to a multilayer perceptron (MLP) neural network with three layers, but the activation function of hidden layer is replaced with multiscaling functions. In experiments performed on the ORL face database it achieved a recognition rate of 97.75% in the presence of facial expression, lighting and pose variations. Results are compared with its wavelet-based counterpart where it obtained a recognition rate of 10.4%. The proposed multiwavenet demonstrated very good recognition rate in the presence of variations in facial expression, lighting and pose and outperformed its wavelet-based counterpart.
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The fingerprints are the more utilized biometric feature for person identification and verification. The fingerprint is easy to understand compare to another existing biometric type such as voice, face. It is capable to create a very high recognition rate for human recognition. In this paper the geometric rotation transform is applied on fingerprint image to obtain a new level of features to represent the finger characteristics and to use for personal identification; the local features are used for their ability to reflect the statistical behavior of fingerprint variation at fingerprint image. The proposed fingerprint system contains three main stages, they are: (i) preprocessing, (ii) feature extraction, and (iii) matching. The preprocessi
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This study was aimed to investigate the load of bacterial contaminant in fresh meat with different types of bacteria.One handered and seven samples were collected from different regions of Baghdad . These samples included 37 of fresh beef 70 of fresh sheep meat. All samples were cultured on different selective media to identitfy of contaminated bacteria .The result revealed that The percentage of bacterial isolate from raw sheep meat were, % 23.8of StreptococcusgroupD,29.4 % of Staphylococcus aureus ,14.7 % of E.coli , %4.9of Salmonella spp, ,%3.5 of pseudomonas aeruginosa, %14.7.%14.7 of Proteus spp.% 2.1 of Listeria spp while the raw beef meat content %5.55 of Staphylococcus aureus, %8.14 of streptococcus group D , %5.18 %1.85 of E.coli,
... Show MoreConstruction is a hazardous industry with a high number of injuries. Prior research found that many industry injuries can be prevented by implementing an effective safety plan if prepared and maintained by qualified safety personnel. However, there are no specific guidelines on how to select qualified construction safety personnel and what criteria should be used to select an individual for a safety position in the United States (US) construction industry. To fill this gap in knowledge, the study goal was to identify the desired qualifications of safety personnel in the US construction industry. To achieve the study goal, the Delphi technique was used as the main methodology for determining the desired qualifications for constructio
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Preparation and Identification of some new Pyrazolopyrin derivatives and their Polymerizations study
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Investigating gender differences based on emotional changes becomes essential to understand various human behaviors in our daily life. Ten students from the University of Vienna have been recruited by recording the electroencephalogram (EEG) dataset while watching four short emotional video clips (anger, happiness, sadness, and neutral) of audiovisual stimuli. In this study, conventional filter and wavelet (WT) denoising techniques were applied as a preprocessing stage and Hurst exponent
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