COMPUTATIONLESS PALM-PRINT VERIFICATION USING WAVELET ORIENTED ZERO-CROSSING SIGNATURE

Palmprints can be characterized by their texture and the patterns of that texture dominate in a vertical direction. Therefore, the energy of the coefficients in the transform domain is more concentrated in the vertical sideband. Using this idea, this paper proposes the characterization of the texture features of the palmprint using zero-crossing signatures based on the dyadic discrete wavelet transform (DWT) to effectively identify an individual. A zero-crossing signature of 4 x 256 was generated from the lower four resolution levels of dyadic DWT in the enrolment process and stored in the database to identify the person in recognition mode. Euclidean distance was determined to find the best fit for query palmprints zero-crossing signature from the dataset. The proposed algorithm was tested on the PolyU dataset containing 6000 multi-spectral images. The proposed algorithm achieved 96.27% accuracy with a lower recognition time of 0.76 seconds. ABSTRAK: Pengesan Tapak Tangan boleh dikategorikan berdasarkan ciri-ciri tekstur dan corak pada tekstur yang didominasi pada garis tegak. Oleh itu, pekali tenaga di kawasan transformasi adalah lebih penuh pada jalur-sisi menegak. Berdasarkan idea ini, cadangan kajian ini adalah berdasarkan ciri-ciri tekstur pada tapak tangan dan tanda pengenalan sifar-silang melalui transformasi gelombang kecil diadik yang diskret (DWT) bagi mengecam individu. Pada mod pengecaman, tanda pengenalan sifar-silang 4 x 256 yang terhasil daripada tahap diadik resolusi empat terendah DWT digunakan dalam proses kemasukan dan simpanan di pangkalan data bagi mengenal pasti individu. Jarak Euklidan yang terhasil turut digunakan bagi memperoleh padanan tapak tangan paling sesuai melalui tanda pengenalan sifar-silang dari set data. Algoritma yang dicadangkan ini diuji pada set data PolyU yang mengandungi 6000 imej pelbagai-spektrum. Algoritma yang dicadangkan ini berjaya mencapai ketepatan sebanyak 96.27% dengan durasi pengecaman berkurang sebanyak 0.76 saat.

Exploring Emotion Recognition Patterns Among Iranian People Using CANTAB as an Approved Neuro-Psychological Assessment

Background: Emotion Recognition is the main component of social cognition and has various patterns in different cultures and nationalities. The present study aimed to investigate emotion recognition patterns among Iranians using the Cambridge Neuro-Psychological Test Automated Battery (CANTAB) as a valid neuropsychological test. Methods: In this descriptive-analytical study, 117 males and females (Mage = 32.1, SD = 6.4) were initially assessed by computerised intelligence and progressive matrices of RAVEN-2. Furthermore, the excitement recognition subtest taken from the Cambridge Neuro-Psychological Test Automated Battery (CANTAB) was performed. The correct response of participants to each of the six basic emotions as well as the recognition time was used for analysis. Results: The maximum correct responses rate was 75.83% related to happy emotion. The correct responses for sadness, surprise, disgust, anger and fear were 70%, 68.48%, 47.84%, 42.54% and 38.26%, respectively. Moreover, the shortest recognition time was related to disgust with 322ms, while sadness with mean response time 1800ms and fear response time with 1529ms indicated the longest recognition time. In addition, participants recognised happiness with mean response time in 1264ms better than other emotions; however, post-hoc t-test analyses showed that only the correct responses for sadness and surprised emotions did not differ significantly, (t (112) = -.59, p = .55, d = .05). These results suggested that different emotions have various correct responses. However, sadness and surprised did not differ. Conclusions: The findings of this study could be beneficial for evaluating cognitive elements, as well as cognitive abilities and inabilities among the Iranian population. Moreover, the findings could be used for investigating social cognition in this population.

Comparative numerical analysis of Bayesian decision rule and probabilistic neural network for pattern recognition

At present, neural networks are increasingly used to solve many problems instead of traditional methods for solving them. This involves comparing the neural network and the traditional method for specific tasks. In this paper, computer modeling of the Bayesian decision rule and the probabilistic neural network is carried out in order to compare their operational characteristics for recognizing Gaussian patterns. Recognition of four and six images (classes) with the number of features from 1 to 6 was simulated in cases where the images are well and poorly separated. The sizes of the training and test samples are chosen quiet big: 500 implementations for each image. Such characteristics as training time of the decision rule, recognition time on the test sample, recognition reliability on the test sample, recognition reliability on the training sample were analyzed. In framework of these conditions it was found that the recognition reliability on the test sample in the case of well separated patterns and with any number of the instances is close to 100 percent for both decision rules. The neural network loses 0,1–16 percent to Bayesian decision rule in the recognition reliability on the test sample for poorly separated patterns. The training time of the neural network exceeds the training time of the Bayesian decision rule in 4–5 times and the recognition time – in 4–6 times. As a result, there are no obvious advantages of the probabilistic neural network over the Bayesian decision rule in the problem of Gaussian pattern recognition. The existing generalization of the Bayesian decision rule described in the article is an alternative to the neural network for the case of non-Gaussian patterns.

Human Action Recognition Technology in Dance Video Image

In order to effectively improve the recognition rate of human action in dance video image, shorten the recognition time of human action, and ensure the recognition effect of dance motion, this study proposes a human motion recognition method of dance video image. This recognition method uses neural network theory to transform and process the human action posture in the dance video image, constructs the hybrid model of human motion feature pixels according to the feature points of human action in the image coordinate system, and extracts the human motion features in dance video image. This study uses the background probability model of human action image to sum the variance of human action feature function and update the human action feature function. It can also use Kalman filter to detect human action in dance video image. In the research process, it gets the human multiposture action image features according to the linear combination of human action features. Combined with the feature distribution matrix, it processes the human action features through pose transformation and obtains the human action feature model in the dance video image to accurately identify the human action in the dance video image. The experimental results show that the dance motion recognition effect of the proposed method is good, which can effectively improve the recognition rate of human action in dance video image and shorten the recognition time.

Research on an Improved MB-LBP 3D Face Recognition Method

In order to improve the accuracy and speed of 3D face recognition, this paper proposes an improved MB-LBP 3D face recognition method. First, the MB-LBP algorithm is used to extract the features of 3D face depth image, then the average information entropy algorithm is used to extract the effective feature information of the image, and finallythe Support Vector Machine algorithm is used to identify the extracted effective information. The recognition rate on the Texas 3DFRD database is 96.88%, and the recognition time is 0.025s. The recognition rate in the self-made depth library is 96.36%, and the recognition time is 0.02s.It can be seen from the experimental results that the algorithm in this paper has better performance in terms of accuracy and speed.

Indeterminate and Enriched Propositions in Context Linger: Evidence From an Eye-Tracking False Memory Paradigm

A sentence such as We finished the paper is indeterminate with regards to what we finished doing with the paper. Indeterminate sentences constitute a test case for two major issues regarding language comprehension: (1) how we compose sentence meaning; and (2) what is retained in memory about what we read in context over time. In an eye-tracking experiment, participants read short stories that were unexpectedly followed by one of three recognition probes: (a) an indeterminate sentence (Lisa began the book), that is identical to the one in the story; (b) an enriched but false probe (Lisa began reading the book); and (c) a contextually unrelated probe (Lisa began writing the book). The probes were presented either at the offset of the original indeterminate sentence in context or following additional neutral discourse. We measured accuracy, probe recognition time, and reading times of the probe sentences. Results showed that, at the immediate time point, participants correctly accepted the identical probes with high accuracy and short recognition times, but that this effect reversed to chance-level accuracy and significantly longer recognition times at the delayed time point. We also found that participants falsely accept the enriched probe at both time points 50% of the time. There were no reading-time differences between identical and enriched probes, suggesting that enrichment might not be an early, mandatory process for indeterminate sentences. Overall, results suggest that while context produces an enriched proposition, an unenriched proposition true to the indeterminate sentence also lingers in memory.

Automated optimum visualization system for construction drawing reading

The task of reading drawings on construction sites has significant efficiency and cost problems. Recent products utilising laser projectors attempt to address the issue of drawing comprehension by projecting full scale versions of the drawings onto 3D surfaces, giving an in-place representation of the steps required to complete a task. However, they only allow projection in red or green at a single brightness level due to the inherent constraints of using a laser-based system, which could cause problems depending on the surface to be projected on and the ambient conditions. Thus, there is a need for a solution that is able to adjust the visualisation parameters of the displayed information based on the surface being projected onto. This study presents a system that automatically changes the visualisation parameters based on the colour and texture of the current surface to make drawings visible under any planar-like surfaces. The proposed system consists of software and hardware, and the software algorithm contains of two parts 1) the optimisation run that computes and updates the visualisation parameters and 2) the detection loop which runs continually and checks if the optimisation run needs to be triggered or not. In order to verify the proposed system, tests on 8 subjects with 4 background surfaces commonly found on site were performed. The test subjects were timed to find 10 bolt holes projected onto the surface using the optimisation system, which was then compared to a control case of black lines projected onto a white background. The system allowed users to complete the task on the real-world backgrounds in the same time as the control case, with the system resulting in up to a 600% decrease in recognition time on some backgrounds.

Feature Level Fusion of Biometric Images Using Modified Clonal Selection Algorithm

In feature level fusion, biometric features must be combined such that each trait is combined so as to maintain feature-balance. To achieve this, Modified Clonal Selection Algorithm was employed for feature level fusion of Face, Iris and Fingerprints. Modified Clonal Selection Algorithm (MCSA) which is characterized by feature-balance maintenance capability and low computational complexity was developed and implemented for feature level fusion. The standard Tournament Selection Method (TSM) was modified by performing tournaments among neighbours rather than by random selection to reduce the between-group selection pressure associated with the standard TSM. Clonal Selection algorithm was formulated by incorporating the Modified Tournament Selection Method (MTSM) into its selection phase. Quantitative experimental results showed that the systems fused with MCSA has a higher recognition accuracy than those fused with CSA, also with a lower recognition time. Keywords: Biometrics, Feature level Fusion, Multibiometrics, Modified Clonal Selection Algorithm, Recognition Accuracy, Recognition Time.

Path Recognition of the Regional Education Expansion Based on Improved Dragonfly Algorithm

To solve the problems of low recognition rate, high misrecognition rate, and long recognition time, the path recognition method of the regional education scale expansion based on the improved dragonfly algorithm is proposed. Through a variety of different behaviors utilized in the optimization process, the dragonfly algorithm model has been constructed. The step size and the position vector are introduced to update the dragonfly’s location. The dragonfly’s foraging behaviors are accurately simulated. Afterward, the dragonfly algorithm is combined with the flower authorization algorithm. The conversion probability is added, and the dragonfly’s global development ability is adjusted in real-time. Then, the dragonfly algorithm is improved. The improved dragonfly algorithm is employed to extract the features of the expansion path of the regional education scale. The improved support vector machine is utilized as a classifier to realize the recognition of the regional education scale expansion path. The experimental results denote that the proposed method has a high recognition rate of the regional education scale expansion path and can effectively reduce the misrecognition rate and shorten the recognition time.

SIMULATION OF RECOGNITION METHOD OF DAMAGED PARTS OF HIGH-INTENSITY SPORTS INJURY IMAGES

ABSTRACT Introduction: When athletes are performing sports training, many movements are of high intensity, and that training is repetitive, resulting in wear and tear on some injured parts. Objective: Sports athletes can damage parts of the body in high - intensity exercise. During the processing, it is necessary to identify and analyze the damaged parts in the image. However, the current relevant methods have low accuracy and different problems of efficiency and quality. Methods: In this paper, a Fish Swarm Algorithm is proposed to identify high-intensity motion damage images. According to the combination of adaptive threshold and mathematical morphology, the contour of the damaged part of the image is extracted. Results: The above-mentioned method can improve the accuracy of identifying damaged parts of sports injury images, shorten the recognition time, and has certain feasibility in determining sports injury parts. Conclusions: This method can be widely used in high-intensity sports injuries. Level of evidence II; Therapeuticstudies - investigation of treatment results.