Human Recognition Methods based on Biometric Technologies

Sharma, Ankita; Chaturvedi, Praveena; Arya, Shwetank

doi:10.5120/21316-4312

Cited by 7 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Euclidean Distance is the distance between two locations on a straight line using the Pythagorean theorem [27]. Equation (1) shows the formula for Euclidean Distance. A distance of two vectors of size n, for example,…”

Section: ) K-nearest Neighbour (Knn)mentioning

confidence: 99%

See 1 more Smart Citation

Gender Classification on Video Using FaceNet Algorithm and Supervised Machine Learning

Adhinata¹,

Junaidi²

2022

IJCDS

View full text Add to dashboard Cite

Gender classification using human face data becomes a trending topic for researchers in the field of image processing and computer vision. The human face is biometric information that can be used to differentiate gender using a computer-aided system. Previous research utilised a local feature algorithm for extracting features on the face. However, the processing speed for one image was more than 2 seconds, making it unsuitable for real-time processing using video data. Processing video data requires a fast feature extraction algorithm because video data collects sequential images (frames). Moreover, the gender classification system's success is also measured by its accuracy, consequently it is necessary to choose the correct classification method to divide the two classes of men and women. In this research, we propose the FaceNet algorithm for feature extraction and explore several supervised machine learning methods (KNN, SVM, and Decision tree) appropriate for gender classification on video data. This study used 23,000 training data on each gender. From the experiment, combination of the FaceNet algorithm and KNN method resulted in the best accuracy of 95.75% with a processing speed of 0.059 seconds on each frame.

show abstract

Section: ) K-nearest Neighbour (Knn)mentioning

confidence: 99%

“…Biometric is a technique that studies physical or human behaviour, often used as an input for pattern recognition [1]. Biometric is biological data that humans have since birth.…”

Section: Introductionmentioning

confidence: 99%

Gender Classification on Video Using FaceNet Algorithm and Supervised Machine Learning

Adhinata¹,

Junaidi²

2022

IJCDS

View full text Add to dashboard Cite

show abstract

“…The security system by utilizing biometric principles, such as iris recognition, is a computational method using biological aspects by displaying the unique characteristics possessed by humans [1]. Scans that utilize these biometric principles will use biometric data to identify individuals based on measurements of their physiological characteristics [2].…”

Section: Introductionmentioning

confidence: 99%

Iris Recognition Using Hybrid Self-Organizing Map Classifier and Daugman’s Algorithm

Saleh

Laia

Gowasa

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

View full text Add to dashboard Cite

One of the neural network algorithms that can be used in iris recognition is self-organizing map (SOM). This algorithm has a weakness in determining the initial weight of the network, which is generally carried out randomly, which can result in a decrease in accuracy when an incorrect determination is made. The solution that is often used is to apply a hybrid process in determining the initial weight of the SOM network. This study takes an approach using the cosine similarity equation to determine the initial weight of the network SOM in order to increase recognition accuracy. In addition, the localization process needs to be carried out to limit the area of the iris image being studied so that it is easy for the recognition process to be carried out. The method proposed in this study for iris recognition, namely hybrid SOM and Daugman’s algorithm, has been tested on several people by capturing the iris of the eye using a digital camera. The captured eyes have been localized first using the Daugman’s algorithm, and then the image features were extracted using the GLCM and LBP methods. In the final stage of the study, an iris recognition comparison test was performed, and the results obtained an accuracy of 85.50% using the proposed method and an accuracy of 73.50% without performing a hybrid process on the SOM network.

show abstract

“…A biometric system enables the identification of a person using distinct feature or characteristic exhibited individual person. The Biometric systems are designed for recognition by taking various physical traits as input such as palm prints, finger prints, face, and the iris [2].…”

Section: Introductionmentioning

confidence: 99%

Principal Componenet Analysis and Local Binary Patterns: A Comparative Study Using Different Databases

2023

IRJMETS

View full text Add to dashboard Cite

This paper compares the efficiency of two popular feature extraction methods Principal Component Analysis and Local Binary Pattern using two different iris databases CASIA and UBIRIS. For classification, Support Vector Machine has been used. The models were tested using 200 iris images. The Receiver Operating Characteristic Curve has been drawn and AUC was calculated. The result shows that LBP achieves better performance with both CASIA and UBIRIS databases compared to PCA. The experiment has been extended by varying the dataset sizes. The result has shown that LBP outperforms PCA with both CASIA and UBIRIS.

show abstract

Human Recognition Methods based on Biometric Technologies

Cited by 7 publications

References 22 publications

Gender Classification on Video Using FaceNet Algorithm and Supervised Machine Learning

Gender Classification on Video Using FaceNet Algorithm and Supervised Machine Learning

Iris Recognition Using Hybrid Self-Organizing Map Classifier and Daugman’s Algorithm

Principal Componenet Analysis and Local Binary Patterns: A Comparative Study Using Different Databases

Contact Info

Product

Resources

About