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Biometric authentication data with three traits using compression technique, HOG, GMM and fusion technique.

ABSTRACT: This paper presents a three trait identification model called multimodal recognition system developed by using different traits like face, finger and voice (Babu and Naidu, 2014, 2016; Balaka and Surendra, 2017) [1-3]. This system provides more security when compare to existing works. Initially, all the traits are followed by pre-processed, extract features using Histogram of oriented gradients (HOG), then apply Gaussian mixture model (GMM) for finding probability density function (PDF) values and then combining these features by using Score level fusion. The result of these features considered as a trainee dataset. In verification process, each test image trait compare with the trainee dataset. This entire process of authentication is done by using machine learning based technique.

SUBMITTER: Naidu BR

PROVIDER: S-EPMC5996745 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Biometric authentication data with three traits using compression technique, HOG, GMM and fusion technique.

Naidu Balaka Ramesh BR Babu Maddali Surendra Prasad MSP

Data in brief 20180331

This paper presents a three trait identification model called multimodal recognition system developed by using different traits like face, finger and voice (Babu and Naidu, 2014, 2016; Balaka and Surendra, 2017) [1-3]. This system provides more security when compare to existing works. Initially, all the traits are followed by pre-processed, extract features using Histogram of oriented gradients (HOG), then apply Gaussian mixture model (GMM) for finding probability density function (PDF) values a ...[more]

PMID: 29900333

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Project description:Biometric technology is becoming increasingly prevalent in several vital applications that substitute traditional password and token authentication mechanisms. Recognition accuracy and computational cost are two important aspects that are to be considered while designing biometric authentication systems. Thermal imaging is proven to capture a unique thermal signature for a person and thus has been used in thermal face recognition. However, the literature did not thoroughly analyse the impact of feature selection on the accuracy and computational cost of face recognition which is an important aspect for limited resources applications like IoT ones. Also, the literature did not thoroughly evaluate the performance metrics of the proposed methods/solutions which are needed for the optimal configuration of the biometric authentication systems. This paper proposes a thermal face-based biometric authentication system. The proposed system comprises five phases: a) capturing the user's face with a thermal camera, b) segmenting the face region and excluding the background by optimized superpixel-based segmentation technique to extract the region of interest (ROI) of the face, c) feature extraction using wavelet and curvelet transform, d) feature selection by employing bio-inspired optimization algorithms: grey wolf optimizer (GWO), particle swarm optimization (PSO) and genetic algorithm (GA), e) the classification (user identification) performed using classifiers: random forest (RF), k-nearest neighbour (KNN), and naive bayes (NB). Upon the public dataset, Terravic Facial IR, the proposed system was evaluated using the metrics: accuracy, precision, recall, F-measure, and receiver operating characteristic (ROC) area. The results showed that the curvelet features optimized using the GWO and classified with random forest could help in authenticating users through thermal images with performance up to 99.5% which is better than the results of wavelet features by 10% while the former used 5% fewer features. In addition, the statistical analysis showed the significance of our proposed model. Compared to the related works, our system showed to be a better thermal face authentication model with a minimum set of features, making it computational-friendly.

Project description:Importance:Many women dread undergoing mammography, and some may not attend or reattend breast cancer screening because of the discomfort or pain induced by breast compression. Objective:To evaluate the noninferiority of the self-compression mammography technique for reducing breast thickness compared with standard compression. Design, Setting, and Participants:This prospective, parallel-group, noninferiority randomized clinical trial was conducted from May 7, 2013, to October 26, 2015, at 6 cancer care centers in France. Participants were women aged 50 to 75 years, without a history of recent breast surgical procedure or treatment, and who could perform self-compression. Analyses were performed on intention-to-treat basis from January 27, 2017, to March 30, 2018. Interventions:Patients were randomized 1:1 to the self-compression group or the standard compression group. Main Outcomes and Measures:Primary end point was breast thickness expressed as the mean of 4 views: right and left craniocaudal and right and left mediolateral oblique. The predefined noninferiority margin was a difference of 3 mm, with a 1-sided 95% CI. Secondary end points included compression force, image quality, requirement for additional views, pain, and patient satisfaction and radiographer assessment questionnaires. Results:Among the 549 women randomized, 548 (97.3%) completed the trial. Of these, 275 (48.8%) (mean [SD] age, 61.35 [6.34] years) were randomized to the self-compression arm and 273 (48.5%) (mean [SD] age, 60.84 [6.41] years) to the standard compression arm. The difference in the mean thickness between the 2 arms was lower than the noninferiority margin, with an upper 1-sided 95% CI less than 3 mm (-0.17; 95% CI,-∞ to 1.89 mm; P < .05). Compression force was higher in the self-compression group compared with the standard compression arm for the 4 mammographic views. Pain was statistically significantly lower in the self-compression group (n = 274) compared with the standard compression group (n = 269) (median [interquartile range (IQR)] score, 2 [1-5] vs 3 [1-5]; P = .009). No difference was reported in the image quality scores of the 2 groups or in the number of additional views performed (median [IQR] extra views, 2 [2-2] vs 2 [2-3] extra views; P = .64), whatever the indication, including insufficient image quality (29 [16.8%] vs 27 [15.0%] insufficient quality views; P = .65). No adverse effects or pain were reported by the participants after the self-compression mammography. Conclusions and Relevance:Self-compression does not appear to be inferior to standard compression mammography in achieving minimal breast thickness without increasing pain or compromising image quality; this technique may be an effective option for women who want to be involved in their breast examination. Trial Registration:ClinicalTrials.gov identifier: NCT02866591.

Dataset Information

Biometric authentication data with three traits using compression technique, HOG, GMM and fusion technique.

Publications

Biometric authentication data with three traits using compression technique, HOG, GMM and fusion technique.

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