Improving Fingerprint Recognition Performance Based on Feature Fusion and Adaptive Registration Pattern

2004 ◽  
pp. 57-66
Author(s):  
Jie Tian ◽  
Yuliang He ◽  
Xin Yang ◽  
Liang Li ◽  
XinJian Chen
Keyword(s):  
Feature Fusion ◽  
Recognition Performance ◽  
Fingerprint Recognition

scholarly journals AN EFFECTIVE COLOR FACE RECOGNITION BASED ON BEST COLOR FEATURE SELECTION ALGORITHM USING WEIGHTED FEATURES FUSION SYSTEM

2013 ◽  
Vol 8 (2) ◽  
pp. 787-795
Author(s):  
Sasi Kumar Balasundaram ◽  
J. Umadevi ◽  
B. Sankara Gomathi
Keyword(s):  
Feature Selection ◽  
Face Recognition ◽  
Feature Fusion ◽  
Recognition Performance ◽  
Feature Selection Method ◽  
Recognition Method ◽  
Color Feature ◽  
Color Component ◽  
Pose Variation ◽  
Color Face Recognition

This paper aims to achieve the best color face recognition performance. The newly introduced feature selection method takes advantage of novel learning which is used to find the optimal set of color-component features for the purpose of achieving the best face recognition result. The proposed color face recognition method consists of two parts namely color-component feature selection with boosting and color face recognition solution using selected color component features. This method is better than existing color face recognition methods with illumination, pose variation and low resolution face images. This system is based on the selection of the best color component features from various color models using the novel boosting learning framework. These selected color component features are then combined into a single concatenated color feature using weighted feature fusion. The effectiveness of color face recognition method has been successfully evaluated by the public face databases.

scholarly journals Human Authentication using Face, Voice and Fingerprint Biometrics

2021 ◽  
Vol 9 (VII) ◽  
pp. 853-862
Author(s):  
Dr. Dinesh Kumar D S
Keyword(s):  
Recognition Performance ◽  
Code Word ◽  
Fingerprint Recognition ◽  
Cascade Algorithm ◽  
Minutiae Extraction ◽  
Fingerprint Biometrics ◽  
Haar Cascade ◽  
Nearest Neighbour Classifier ◽  
Matching Score ◽  
The Voice

Multimodal biometric approaches are growing in importance for personal verification and identification, since they provide better recognition results and hence improve security compared to biometrics based on a single modality. In this project, we present a multimodal biometric system that is based on the fusion of face, voice and fingerprint biometrics. For face recognition, we employ Haar Cascade Algorithm, while minutiae extraction is used for fingerprint recognition and we will be having a stored code word for the voice authentication, if any of these two authentication becomes true, the system consider the person as authorized person. Fusion at matching score level is then applied to enhance recognition performance. In particular, we employ the product rule in our investigation. The final identification is then performed using a nearest neighbour classifier which is fast and effective. Experimental results confirm that our approach achieves excellent recognition performance, and that the fusion approach outperforms biometric identification based on single modalities.

scholarly journals An investigation of latent fingerprinting techniques

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ritika Dhaneshwar ◽  
Mandeep Kaur ◽  
Manvjeet Kaur
Keyword(s):  
Law Enforcement ◽  
Recognition Performance ◽  
Forensic Analysis ◽  
Fingerprint Recognition ◽  
Main Body ◽  
Digital Era ◽  
Benchmark Datasets ◽  
Multiple Challenges ◽  
Reliability And Robustness

Abstract Background Latent fingerprints are the unintentional impressions that are left at crime scenes, which are considered to be highly significant in forensic analysis and authenticity verification. It is an extremely crucial tool used by law enforcement and forensic agencies for the conviction of criminals. However, due to the accidental nature of these impressions, the quality of prints uplifted is generally inferior. Main body In order to improve the overall fingerprint recognition performance, there is an insistent need to design novel methods to improve the reliability and robustness of the existing techniques. Therefore, a systematic review is presented to study the existing methods for latent fingerprint acquisition, enhancement, reconstruction, and matching, along with various benchmark datasets available for research purposes. Conclusion The paper highlights multiple challenges and research gaps using comparative analysis of existing enhancement, reconstruction and matching approaches in order to augment the research in this direction that has become imperative in this digital era.

scholarly journals Research on Recognition Method of COVID-19 Images Based on Deep Learning

2020 ◽  
Author(s):  
dongshen ji ◽  
yanzhong zhao ◽  
zhujun zhang ◽  
qianchuan zhao
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Feature Fusion ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Small Sample ◽  
Experimental Results ◽  
Ct Image ◽  
Recognition Method ◽  
Sample Recognition

In view of the large demand for new coronary pneumonia covid19 image recognition samples,the recognition accuracy is not ideal.In this paper,a new coronary pneumonia positive image recognition method proposed based on small sample recognition. First, the CT image pictures are preprocessed, and the pictures are converted into the picture formats which are required for transfer learning. Secondly, perform small-sample image enhancement and expansion on the converted picture, such as miscut transformation, random rotation and translation, etc.. Then, multiple migration models are used to extract features and then perform feature fusion. Finally,the model is adjusted by fine-tuning.Then train the model to obtain experimental results. The experimental results show that our method has excellent recognition performance in the recognition of new coronary pneumonia images,even with only a small number of CT image samples.

The Design of Library Management System Based on Internet of Things and Hand Lines Recognition System

2014 ◽  
Vol 672-674 ◽  
pp. 1985-1990 ◽  
Author(s):  
Wang Fang
Keyword(s):  
Management System ◽  
Iris Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Library Management ◽  
Material Resources ◽  
Library Management System ◽  
Biometric Systems

For an ordinary individual biometric systems and technology, such as fingerprint recognition, palm recognition, face recognition or iris recognition, or late detection from a single object has crippled so that they have the characteristics of unity and limitations, this paper combining fingerprint and hand palm pattern recognition technology, taking into account the complexity of the image pattern and diversity, we propose a dual recognition algorithm, which greatly makes up for lack of a single fingerprint or palm print recognition method. The technology used in library management system than traditional card-borrowed books have higher efficiency and save manpower and material resources. After the experimental statistics, and achieved the desired results, not only improve the recognition efficiency, but also to ensure the accuracy of the recognition performance.

Adaptive Fusion of Multi-Biometrics for Human Identification in Video

2015 ◽  
Vol 719-720 ◽  
pp. 1013-1018
Author(s):  
Ying Hui Kong ◽  
Pei Yao Chen
Keyword(s):  
Feature Fusion ◽  
Recognition Performance ◽  
Decision Fusion ◽  
Fusion Method ◽  
Recognition Method ◽  
Fusion Rules ◽  
Discriminating Power ◽  
Adaptive Fusion ◽  
Biometric Fusion ◽  
Fusion Features

The purpose of multiple biometric fusion is to improve the recognition performance by utilizing their complementary. In this paper, the feature fusion recognition method of multi-view face and gait in video is studied, and a adaptive decision fusion method is proposed. The results showed that the adaptive fusion features carry the most discriminating power compared to any individual biometric and other static fusion rules like Max and Sum.

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