Combining of feature extraction for real-time facial authentication system

Intan, Indo

doi:10.1109/citsm.2017.8089223

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…It describes the face segmented such as mouth, nose, and eyes with their geometry distribution [11], [12]; iii) face recognition as the last step to consider the feature extracted and compares it faces image stored in a faces database (faces gallery) [11]. The other word, a face recognition is a classification process to recognize probe face as one of the number faces in the stored face gallery [13]. Previous research Table 1 has not completed the integration of edge detection in the accumulation of the pixel value of the image, only comparing singly or combining two or three edge detections.…”

Section: Introductionmentioning

confidence: 99%

Facial recognition using multi edge detection and distance measure

Intan

Nurdin

Pangerang

2023

IJ-AI

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Face recognition provides broad access to several public devices, so it is essential in the midst of today's technology boom. Human face recognizing has challenge in using uncomplicated and straightforward algorithms quickly, using memory specifications are not too high, otherwise the results are quality and accurate. Face recognition using combination edge detection and Canberra distance can be recommended for applications that require fast and precise access. The application of several edge detections singly has low performance, so it requires a combination technique to obtain better results. The proposed method combined several edge detections such are Robert, Prewitt, Sobel, and Canny to recognize a face image by identification and verification. As a feature extractor, the combination edge detection forms a more robust and more specific facial pattern on the contour lines. The results show that the combination accuracy outperforms other extractor features significantly. Canberra distance produces the best performance compared to Euclidean distance and Mahalanobis distance.

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Section: Introductionmentioning

confidence: 99%

Facial recognition using multi edge detection and distance measure

Intan

Nurdin

Pangerang

2023

IJ-AI

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“…Similarly, holistic appearance approaches take into account the picture of the face. Such a methodology is unique in the way facial images work and helps to treat faces in a different way from other categories discussed in [8]; therefore, it is not responsible for the independent processing of functions. is approach has been used by various researchers in facial recognition using infrared imagery.…”

Section: Introductionmentioning

confidence: 99%

Smart Identity Management System by Face Detection Using Multitasking Convolution Network

Farhi

Abbasi

Rehman

2021

Security and Communication Networks

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Identity management system in most academic and office environments is presently achieved primarily by a manual method where the user has to input their attendance into the system. The manual method sometimes results in human error and makes the process less efficient and time-consuming. The proposed system highlights the implementation and design of a smart face identification-based management system while taking into account both the background luminosity and distance. This system detects and recognizes the person and marks their attendance with the timestamp. In this methodology, the face is initially resized to 3 different sizes of 256, 384, and 512 pixels for multiscale testing. The overall outcome size descriptor is the overall mean for these characteristic vectors, and the deep convolution neural network calculates 22 facial features in 128 distinct embeddings in 22-deep network layers. The pose of the 2D face from −15 to +15° provides identification with 98% accuracy in low computation time. Another feature of the proposed system is that it is able to accurately perform identification with an accuracy of 99.92% from a distance of 5 m under optimal light conditions. The accuracy is also dependent on the light intensity where it varies from 96% to 99% under 100 to 1000 lumen/m2, respectively. The presented model not only improves accuracy and identity under realistic conditions but also reduces computation time.

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Classification of Thyroid Carcinoma using Sobel Edge Detection and Adaptive Neuro Fuzzy Inference System Approach

Intan

Soetikno

2019

2019 7th International Conference on Cyber and IT Service Management (CITSM)

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Combining of feature extraction for real-time facial authentication system

Cited by 4 publications

References 11 publications

Facial recognition using multi edge detection and distance measure

Facial recognition using multi edge detection and distance measure

Smart Identity Management System by Face Detection Using Multitasking Convolution Network

Classification of Thyroid Carcinoma using Sobel Edge Detection and Adaptive Neuro Fuzzy Inference System Approach

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