Correct Acceptance Rate Research Articles

Vis-NIR hyperspectral imaging combined with incremental learning for open world maize seed varieties identification

There are many maize seed varieties circulating on the Chinese market, some varieties will be added and eliminated by the government every year, and fake varieties cannot be exhaustively listed. Furthermore, the updating of some varieties requires retraining the entire model, which is time-consuming and laborious. This poses a huge challenge for the authenticity identification of varieties and online model updating. Herein, hyperspectral imaging (HSI) combined with incremental learning (IL) was used to solve this problem. A novel radial basis function-biomimetic pattern recognition (RBF-BPR) model for IL was proposed and compared with one-class support vector machine (OCSVM) and BPR models under two test schemes. Hyperspectral images of five varieties of maize seeds were collected, and convolutional autoencoder (CAE) was used to extract features to remove redundant information and improve the generalization ability of models. A unique hybrid model was designed for each variety respectively, and the IL process was simulated. In two test schemes, the overall correct acceptance rate (CAR) for the known varieties and the overall correct rejection rate (CRR) for the unknown varieties of CAE-RBF-BPR model both reached 100%, which was superior to CAE-OCSVM and CAE-BPR models. Especially after RBF was used to map data, the performance of RBF-BPR model had a qualitative improvement compared with the original BPR model. In summary, the proposed method can realize IL without accessing the old classes data, while meeting the requirements of identifying known varieties and rejecting unknown varieties. In addition, if some varieties are eliminated by the government in the future, the corresponding models can also be removed form the whole system. The combination of such method and HSI has a broad application prospect in the identification of maize seed varieties, thus avoiding the trouble of retraining entire model when some maize seed varieties need to be updated.

Application of fingerprint encryption algorithm based on multi-feature extraction

The area of fingerprint image collected by mobile intelligent device is small, which will lead to the lack of extracted fingerprint feature information. In this study, a small area fingerprint encryption algorithm based on fingerprint mosaic was proposed. The experimental results on the real fingerprint database show that the proposed fingerprint mosaic method can significantly improve the performance of the small area fingerprint encryption algorithm, the acquisition failure rate decreased from 39% to 8%, and the correct acceptance rate increased from 48% to 69%. The algorithm uses fingerprint feature mosaic technology and fingerprint image mosaic technology to effectively increase fingerprint feature information.

Development and Analysis of a Zeta Method for Low-Cost, Camera-based Iris Recognition

Iris recognition is an alternative authentication method. Many studies have tried to improve iris recognition as a biometric-based alternative for secure authentication. Iris segmentation is an important part of iris recognition because it defines the image region that is used for subsequent processing such as feature extraction and matching, hence directly affects the overall iris recognition performance. This work focuses on the development of an authentication system using localization methods and half-polar normalization of the iris. The proposed Zeta method uses a new model of eye segmentation and normalization that can be used simultaneously on both eyes, considering different iris patterns in those two eyes. There are seven variants of the proposed and tested Zeta method: Zeta-v1, Zeta-v2, Zeta-v3, Zeta-v4, Zeta-v5, Zeta-v6, and Zeta-v7. Overall, the method achieved an average segmentation time performance of 0.0138427 seconds. The most accurate rate was by the Zeta-v1 method, with a value threshold of 100% on the wrong rejection rate and 94.9% on the correct acceptance rate.

Identification of maize haploid kernels based on hyperspectral imaging technology

Haploid breeding is a significant technology of maize breeding. Rapid and accurate haploid kernel identification method has great significance to accelerating the efficiency of haploid breeding. At present, detecting genetic markers on the embryo of kernels by machine vision and determining oil content of maize kernels by nuclear magnetic resonance (NMR) are widely used to automatically identify haploid maize kernel. However, the machine vision method can only identify the haploid through embryo side of kernels, and the NMR method cannot distinguish them when haploid and diploid have the overlap oil content. The study was aimed exploring a rapid and accurate method to identify haploid maize kernel using near-infrared hyperspectral imaging technology to overcome the limitations of current automated haploid identification and to achieve more accurate screening of haploid. In terms of two representative varieties of maize (Zhengdan 958 and Nongda 616), the study adopted spectral features of hyperspectral imaging to discuss the influence of embryonic orientation (embryo faces to or against light source) on haploid identification model. Meanwhile, the separability of embryo and non-embryo and identification accuracy of joint modeling of embryo and non-embryo were analyzed. The study showed that the greater difference between embryo and non-embryo of haploid and diploid, but hyperspectral imaging method could effectively distinguish haploid and diploid through embryo or non-embryo. At the same time, with the qualitative analysis method, two maize varieties could accurately distinguished haploid and diploid with overlapping oil content based on joint modeling. In this case, the test set of haploid and diploid achieved yielded higher correct acceptance rate (CAR) of 99% and the false acceptance rate (FAR) were both below 1%, with a high accuracy rate. The study showed that it is feasible to recognize maize haploid using hyperspectral imaging technology, which can provide a reference for the later haploid sorting systems.

User verification based on the support vector machine using intra-body propagation signals

Use of intra-body propagation signals has been proposed for biometric authentication. However, verification performance of the conventional method is low. To overcome this limitation, this study introduces the support vector machine (SVM) into the verification process, which improves the verification rate to approximately 83%. However, the correct acceptance rate of genuine users using only SVM is 49%, which is too low for practical applications. Thus, we introduce the concept of one versus one (1vs1) SVM. Each 1vs1 SVM distinguishes a genuine (authorised) user from another (unauthorised) user. Verification is achieved on the basis of a majority rule using plural 1vs1 SVMs related to a genuine user. The correct acceptance rate is greatly improved to 84% while maintaining equivalent verification performance. As a result, it is further confirmed that an intra-body propagation signal is a potential new biometric trait.

A segmentation-free biometric writer verification method based on continuous dynamic programming

By introducing the continuous dynamic programming (CDP) algorithm developed by Oka [Oka, R., 1998. Spotting method for classification of real world data. The Comput. J. 41 (8), 559–565], we have developed a new segmentation-free, text-independent biometric writer verification method improving the average correct acceptance rate and the average correct rejection rate of forgeries to a practically useful level of about 95% and 100%, respectively, outperforming the only other biometric text-independent writer verification method of Yamazaki and Komatsu [Yamazaki, Y., Komatsu, N., 1999. Extraction of personal features from stroke shape, writing pressure and pen inclination in ordinary characters. In: Proc. 5th Internat. Conf. on Document Analysis and Recognition (ICDAR1999), pp. 426–429] based on a P-type Fourier descriptor-based learning vector quantisation scheme. To implement a text-independent, DP-based biometric writer verification, our spotting-enabling CDP algorithm has not only successfully traced and tracked an optimal microscopic dynamic features of real-time writing processes of either scanned characters or images of relevant individuals but also has implemented extension to a more secure text-independent writer verification method by exploiting the built-in spotting function of the CDP method, which is capable of ignoring inputs outside the task domain. This is so because a text-independent sample can be selected from any of many possible candidates located at an arbitrary location within the specified long reference sample. We have demonstrated the applicability of the method not only to Japanese text but also to English text that tends to assess the language independence of the method.

Semantic confidence measurement for spoken dialog systems

This paper proposes two methods to incorporate semantic information into word and concept level confidence measurement. The first method uses tag and extension probabilities obtained from a statistical classer and parser. The second method uses a maximum entropy based semantic structured language model to assign probabilities to each word. Incorporation of semantic features into a lattice posterior probability based confidence measure provides significant improvements compared to posterior probability when used together in an air travel reservation task. At 5% False Alarm (FA) rate relative improvements of 28% and 61% in Correct Acceptance (CA) rate are achieved for word level and concept level confidence measurements, respectively.

Assessment of sensitivity and specificity of clinical evaluation of cast restoration marginal accuracy compared to stereomicroscopy

One of the most important criteria for the clinical acceptability and longevity of cast restorations is marginal adaptation. However, there is limited information on the accuracy of commonly practiced evaluation techniques. This study assessed the effect of preparation design on marginal adaptation. In addition, sensitivity and specificity of clinical evaluations of marginal adaptations of cast restorations were assessed and compared to stereomicroscopy. Three Ivorine molar teeth of differing designs were prepared: (1) a complete crown preparation with a chamfer finish line, (2) a complete crown preparation with a buccal shoulder and beveled finish line, and (3) a three-quarter crown preparation with proximal boxes and beveled finish line. Twenty-four castings (n = 8) were prepared (Ney technique casting metal). Twelve circumferential sites were identified for examination on each casting using a stereomicroscope, which was interpreted using software (Bioquant 98). The clinical examinations included explorer examination to detect marginal gap by tactile feel, followed by examination with a disclosing media (GC FitChecker). Chi-square tests of independence were used to evaluate the effect of preparation design and compare agreement between examination methods for detection of marginal gap size of greater than or equal to 30 microm (alpha=.05). The overall marginal adaptation of preparation designs was not significantly different. Results of explorer examination at 30-microm sensitivity and specificity were 46% and 97%, respectively. Similar values for disclosing media at 30 microm were 12% sensitivity and 97% specificity. The overall percent agreement with the stereomicroscope using the explorer was 58.3%, with 23.6% correct acceptance rate and 34.7% correct rejection rate. With respect to detection of marginal fit, the combination of disclosing material with explorer data did not yield significantly improved results (59.4%), as compared to explorer alone. A receiver operator characteristic curve yielded satisfactory sensitivity (0.8) and specificity (0.9) at a gap size value of 124 microm. Within the limitations of this study, the preparation design did not significantly affect the marginal adaptation. Clinical detection, with similar sensitivity and specificity as the stereomicroscope, occurred at marginal openings greater than or equal to 124 microm. Therefore, commonly used clinical evaluation techniques using an explorer and disclosing media may be inadequate for assessments of marginal accuracy.

Lock control system based on face identification

Reliable and feasible security systems are needed at limited-access entrances. We have made an experimental lock-control system that unlocks the door when the system identifies a registered person's face. The face identification procedure was designed for practical use. There are three main processes in the face identification: 1) extraction of the face, 2) normalization of the size and inclination of the face image, and 3) matching of facial features (eye, nose, and mouth). In order to achieve high reliability, the normalization process uses inner corners of eyes as reference points for affine transformation. Matching is performed on the characteristic facial features (eye, nose, and mouth) for exclusion of features that are easy to imitate, such as eyeglass frames and hairstyle. Thresholds for the matching are determined by statistical estimation. In experiments, the system realized a correct acceptance rate of 92.2% and a correct rejection rate of 99.6%.

Lock control system based on face identification

Reliable and feasible security systems are needed at limited-access entrances. We have made an experimental lock-control system that unlocks the door when the system identifies a registered person's face. The face identification procedure was designed for practical use. There are three main processes in the face identification: 1) extraction of the face, 2) normalization of the size and inclination of the face image, and 3) matching of facial features (eye, nose, and mouth). In order to achieve high reliability, the normalization process uses inner corners of eyes as reference points for affine transformation. Matching is performed on the characteristic facial features (eye, nose, and mouth) for exclusion of features that are easy to imitate, such as eyeglass frames and hairstyle. Thresholds for the matching are determined by statistical estimation. In experiments, the system realized a correct acceptance rate of 92.2% and a correct rejection rate of 99.6%.