Umesh Bhadade

Bio: Umesh Bhadade is an academic researcher. The author has contributed to research in topics: Biometrics & Radon transform. The author has an hindex of 4, co-authored 6 publications receiving 70 citations.

Multimodal score level fusion for recognition using face and palmprint

Abstract: The paper proposes a t-norm-based matching score fusion approach for a multimodal heterogenous biometric recognition system. Two trait-based multimodal recognition system is developed by using biom...

Biometric Recognition Using Fusion

Abstract: Human identification systems based on biometrics are used in many applications to increase the security level. There are different biometric traits which are used in various applications. Monomodal biometric systems face many challenges such as error rates, using only single biometric for human recognition. Today, to increase the security of the authentication system, various multimodal biometric systems are proposed. A multimodal biometric system uses more than one biometric trait or modality for recognition of an individual. Multimodal biometric systems fuses different types of input at different level: Score level, Feature level and Decision level to get the better performance of the system.

Multimodal system using Radon-Gabor transform

Abstract: Reliable person recognition schemes are demanding in variety of applications, either to confirm or determine the identity of an individual. This is still a challenging problem, addressed by many researchers using unimodal biometric. However, research shows that the performance of unimodal biometric systems is limited. To improve the performance, in the proposed work, multiple biometric modalities of a person are used. Particularly, face and palmprint are used as a biometric trait for the person recognition task. Multiresolution features based on Radon Gabor transform are proposed in this work. As the Radon transform is invariant to rotation and a Gabor transform provides the multiple orientations, the proposed feature improves the recognition rate up to 98%. At feature level, the fusion of face and palmprint is used. The Performance of the proposed system is evaluated on IITD, PolyU_Palmprint and Face 94 databases.

Face and Palmprint Biometric Recognition by using Weighted Score Fusion Technique

Abstract: A multimodal fusion biometric verification system for face and palmprint modalities is proposed. The goal is to achieve a higher Accuracy for standard Databases. Fusion is done at score level using feature extraction algorithms such as, Radon transform, Ridgelet transform, TPLBP, FPLBP HOG, Gabor filter and DCT. Experiments are conducted on face94, face95, face96, FRGC IITD and PolyU databases. Only 1 image is given as a training set for each subject in respective databases. Matching Algorithm is used so as to achieve maximum GAR (Genuine acceptance rate). The results are discussed further in the paper. The accuracy achieved is 99.6% for FAR (False Acceptance rate) of 0.1%. Experimental results indicate that this approach although simple yet can achieve a greater accuracy.

Dual Palm Print-Based Human Recognition Using Fusion

Abstract: Palmprint recognition system has many potential applications in human recognition/identification. The proposed work is academically very modest yet competent palmprint identification system based on combination of left and right palmprint. Here the presented work consists of feature fusion technique and score fusion technique. The feature fusion consists of two techniques of fusion and one score fusion technique. Experimentation performed on the Indian Institute of Technology, Delhi (IITD), palmprint database. The resulting accuracy is 98.81% which is achieved for the TPLBP-based feature fusion.

Fingerprint Image Enhancement and Minutiae Extraction

Abstract: Automatic and reliable extraction of minutiae from fingerprint images is a critical step in fingerprint matching.The quality of input fingerprint images plays an important role in the performance of automatic identification and verification algorithms.Presents a fast fingerprint enhancement and minutiae extraction algorithm which improves the clarity of the ridge and valley structures of the input fingerprint images based on the frequency and orientation of the local ridges and thereby extracts correct minutiae.Experimental results show that the method performs well.

Optimized video internet of things using elliptic curve cryptography based encryption and decryption

Abstract: The adaptive Joint Photographic Experts Group 2000 (JPEG2000) image compression approach employing the wavelet image transform had proposed with the rise of the optimized Video Internet of Things (VIoT) using image transmission security using Elliptic Curve Cryptography (ECC) techniques. Compressive Sensing (CS) for periodic data transfers has shown to be an excellent option for Wireless Sensor Networks (WSNs) since CS-based sensor communications drastically reduce data transmissions and enhance energy efficiency. However, another issue that arises when utilizing the optimized VIoT with image transmission compression is data loss as a result of various security risks during transmission. The numerous cooperative communication strategies proved their feasibility in different ways. However, additional issues must be handled while handling image transmission in VIoT. For example, when preparing to transmit an image, you may expend a lot of energy. The main objective of this paper is to increase image quality while minimizing processing time and error rates. However, force aptitude is vital to the research problem for the Wireless Multimedia Sensor Network (WMSN), as high-dimensional digital images use greater processing capabilities of sensor nodes. The image in WMSN is transmitted through a large number of relays. The experimental findings demonstrate the effectiveness of the suggested paradigm.

Detection of human genetic variation in VAC14 gene by ARMA-PCR technique and relation with typhoid fever infection in patients with gallbladder diseases in Thi-Qar province/Iraq

Abstract: Our study screen for S. Typhi present in the gallbladder of (2 0 0) patients suffering from GD along with 40 normal gallbladders, the samples were taken and diagnosed at the Al-Hussein teaching and Al-Amel hospitals, as well as the Noor Al Hussein and Ibn Al-Baitar laboratories, in Thi-Qar Province, Iraq at the period between February and October 2020, from both sexes, with ages ranging from 13 to 98 years. Histology along with the microbiological and molecular screening of specimens by PCR method for S. Typhi detection in samples. On histopathological observations, the most common GD were cholelithiasis (69%) than acute acalculous cholecystitis (without stones) (18.5%) while (12.5%) were suffering from gallbladder cancer .they were all undergoing clinically and CT examination and PCR assay for screening of S.Typhi in GD patients appeared (13.06%) of them suffering from typhoid fever. Investigation VAC14 gene mutation in GD patients infected with Typhoid fever by gene polymorphism ARMS-PCR technique revealed that 10 (38.46%) cases of GD patients infected with S. Typhi carry a mutation in the VAC14 gene while only 3(11.5%) cases of GD patients not infected with S. Typhi recorded a mutation in VAC14 gene and recorded that GD patients infected with typhoid fever that has about five times the risk of a gene VAC14 mutation than the incidence of the same mutation in GD patients without typhoid fever and the patient infected with typhoid fever that contains cdtB gene have ten times the risk of a gene VAC14 mutation than the incidence of the same mutation in a patient infected with typhoid fever that not contain cdtB gene.

The critical human behavior factors and their impact on knowledge management system–cycles

Abstract: PurposeThis study attempts to find out the impact of the human behavioral factors (HBFs) including emotion, factors of deals with processes within and between groups as well as with the impact of these processes on individuals’ attitudes and moods, personality, beliefs and values, perception and motivation on the knowledge management system–cycles (KMS-Cs) which comprises sharing; it considers findings from social psychology and discusses their applicability in knowledge management (KM) research and practice; social psychological concepts that strongly influence knowledge processes in organizations are first introduced. It is creating, storing and transferring of academic staff while analyzing the certificates on the acquired behaviors and knowledge which were involved in each of the communications, decision-making, creating new ideas, providing new knowledge, idea diversity, progressing, enhancing and improving the organization, using up-to-date technology and proactivity between the independent and dependent variables. In order to test the study hypotheses, data of 219 respondents working at the University of Sulaimani were collected. The results of the study revealed the academic staff psychology effect on KMS-Cs with a substantial relationship between the HBFs and cycles of KM during academic and administrative work. Also, it surged their academic staff efficiency through a conceptual model called KM behavior (KMB); knowledge management systems (KMSs) are applications of the organization's communication and information systems (CISs) designed to support the various KM processes. They are generally not technologically distinct from the CISs but rely on databases, such as those designed to put organizational participants in contact with recognized experts in a variety of topic areas (Yakan, 2008; Al Hayani, 2020). Information technology (IT) used in KM is known as KMS. In general, KMSs are computer systems that enable organizations to manage knowledge that is efficient and cost-effective. KMS is a class of information systems applied to the management of organizational knowledge. KMS is a system that increases organizational performance by enabling employees to make better decisions when applying their knowledge as part of their daily business activities.Design/methodology/approachResearch hypotheses Ho: HBFs and KMS-Cs are not correlated. H1: HBFs have no impact on KMS-Cs. H2: certificates have no effect on HBFs and KMS-Cs. Data collection and sample demographics: in this study, the relevant information for assessing the HBFs and their impact on the KMS-Cs was gathered through a questionnaire survey. The HBF was measured using the following items: emotions, attitudes and moods, personality, beliefs and values, perception and motivation. The knowledge management cycle (KMC) was measured using the following items: knowledge sharing, knowledge creation, knowledge storing and knowledge transfer. The total number of employees at the University of Sulaimani, Sulaimaniya, at the time of data collection (May, 2019) was 117. Since the information available on the number of academic staff at the University of Sulaimani is according to the departments, this study employed a proportionate stratified random sampling method to select the number of academic staff from colleges and departments at the University of Sulaimani. The total number of academic staff at the University of Sulaimaniis is 1,740. Therefore, the appropriate sample size for this study is at least 5% of the population (i.e. 90 respondents) (Langham, 1999). The questionnaire was administered personally through Google Form where questionnaires were collected from the respondents. Examination of the response rate shows that the response rate for this study is excellent. The research instrument consists of two main sections. The first section incorporates a nominal scale to identify respondents' demographic information. The second section uses the five-point Likert-type scale from fully disagree (1) to fully agree (5). All of the measurement items went through backward translation (translated from English into Korean and back into English) to ensure consistency and to resolve discrepancies between the two versions of the instrument (Mullen, 1995; Aldiabat et al., 2018). The participants were almost equal in terms of gender, 59 were males and 58 were females. The certificate for each one of the PhD, MSc and BSc was 39 participants. The number of participants whose age was between 23 and 32 years was 26, between 33 and 42 years was 50, between 43 and 52 years was 29, between 53 and 62 years was 10 and above 62 years was 2. Validity and reliability: in addition to the steps mentioned earlier to assess the validity and reliability of the study tools, a further test was executed. The reliability to measure many inner variables in regularity, Cronbach’s alpha is generally utilized in order to evaluate it and the value should exceed 0.70 for each variable (Alharbi, and Drew, 2014) (Table 1). Cronbach's alpha regards to the test of reliability of a skill for each of the HBF and KMC.FindingsThe study is considered the organizations relationship between HBFs and KMS-Cs and the influence of the factors on the cycles. So, the new ideas emerge to create knowledge about product development among employees. The group experience works as an essential element (Grimsdottir and Edvardsson, 2018). Knowledge resides in human minds and, as a result, employee behavior and explanatory skills are the key drivers of KM (Prieto and Revilla, 2005). First, knowledge creation, sharing and storing is increased when the organization has motivated the employees. Second, knowledge is shared rapidly when the employees have owned a strong personality, new idea, impression and perception. Third, both the beliefs and values lead to creating new knowledge when the employees obtained it inside the organization. Then, the emotion factors illustrated the weak relation with knowledge sharing, knowledge creation, knowledge storing and knowledge transfer.Originality/valueKnowledge is considered as a great factor in achieving organizational goals (Hammami and Alkhaldi, 2017). Therefore, this study has explained that knowledge is an essential element for employees and organizations. Furthermore, it progresses the skills and capabilities during the job. Nevertheless, this knowledge is impacted through human behaviors because the behavior evolves crucial factors that help the academic staff to create, share, store and transfer the knowledge through motivation, perception, personality, attitudes, moods, beliefs and values. Knowledge sharing is a culture of social interaction involving the exchange of knowledge, experiences and skills of employees across the organization (Zugang et al., 2018). Organizations need to pay particular attention to the method of communication used where knowledge becomes useless if employees are not encouraged to study and use it in their daily activities (Boatca et al., 2018). Knowledge sharing can be achieved by taking into account technical standards (KMS), social standards (environment) and personality (motivation) (Özlen, 2017).

Multimodal biometric authentication using fingerprint and iris recognition in identity management

Abstract: The majority of deployed biometric systems today use information from a single biometric technology for verification or identification. Large-scale biometric systems have to address additional demands such as larger population coverage and demographic diversity, varied deployment environment, and more demanding performance requirements. Today's single modality biometric systems are finding it difficult to meet these demands, and a solution is to integrate additional sources of information to strengthen the decision process. A multibiometric system combines information from multiple biometric traits, algorithms, sensors, and other components to make a recognition decision. Besides improving the accuracy, the fusion of biometrics has several advantages such as increasing population coverage, deterring spoofing activities and reducing enrolment failure. The last 5 years have seen an exponential growth in research and commercialization activities in this area, and this trend is likely to continue. Therefore, here we propose a novel multimodal biometric authentication approach fusing iris and fingerprint traits at score-level. We principally explore the fusion of iris and fingerprint biometrics and their potential application as biometric identifiers. The individual comparison scores obtained from the iris and fingerprints are combined at score-level using a three score normalization techniques (Min-Max, Z-Score, Hyperbolic Tangent) and four score fusion approaches (Minimum Score, Maximum Score Simple Sum and User Weighting). The fused-score is utilized to classify an unknown user into the genuine or impostor.