Face image quality assessment based on photometric features and classification techniques

Khastavaneh, Hassan; Ebrahimpour-Komleh, Hossein; Joudaki, Majid

doi:10.1109/kbei.2017.8324988

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Proposing a deep feature-based approach, 18 extracts deep features from face images and selects the most representative features via sparse coding, while a support vector regression (SVR) model computes the resulting face quality scores. Similarly, 19 utilizes deep feature extraction, representative feature selection through sparse coding, and SVR-based face quality scoring. 20 presents a CNN-based key frame engine, trained based on recognition scores from a deep face recognition system.…”

Section: Related Workmentioning

confidence: 99%

Efficient storage and retrieval of high-quality face images for effective face recognition

Okcu,

Sari,

Çigla

2023

Artificial Intelligence for Security and Defence Applications

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Recent developments in chip technology have led to the integration of smart Artificial Intelligence (AI) capabilities in surveillance cameras such as vehicle and pedestrian detection, license plate recognition, and face detection (FD). FD cameras are being widely deployed in various public locations to identify wanted individuals. However, the large size of identity databases and the need for personal information security make it necessary to perform face recognition (FR) on servers rather than on cameras. On the other hand, more FD cameras and detected faces lead to extensive power consumption and computational power demand for real-time FR along with large storage devices to store face images. While FD cameras track detected faces and select the best quality face for FR, multiple faces belonging to the same identities might be sent to the servers due to track algorithms or reentering of identity to the scene. Therefore, we propose a method for finding similar faces belonging to the same identities and removing low-quality faces to keep only the highest-quality face in the server for storage and FR. We utilize facial embedding vectors, obtained from aligned faces using an FR model, and store them in a database along with face image information such as face capture time, camera IP, and face quality score. If a face has similar faces in the database, the highest-quality face is kept in the database and others are removed. As a result, our proposed method eliminates extra face images and keeps the highest-quality face images for high performance, efficient FR, and effective storage.

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Section: Related Workmentioning

confidence: 99%

Efficient storage and retrieval of high-quality face images for effective face recognition

Okcu,

Sari,

Çigla

2023

Artificial Intelligence for Security and Defence Applications

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An Efficient Method for Face Quality Assessment on the Edge

Okcu

Özkalaycı

Çığla

2020

Lecture Notes in Computer Science

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Digital Image Classification Techniques

Shrivastav

Singh

2019

Advances in Social Networking and Online Communities

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Image classification is a technique to categorize an image in to given classes on the basis of hidden characteristics or features extracted using image processing. With rapidly growing technology, the size of images is growing. Different categories of images may contain different types of hidden information such as x-ray, CT scan, MRI, pathologies images, remote sensing images, satellite images, and natural scene image captured via digital cameras. In this chapter, the authors have surveyed various articles and books and summarized image classification techniques. There are supervised techniques like KNN and SVM, which classify an image into given classes and unsupervised techniques like K-means and ISODATA for classifying image into a group of clusters. For big images, deep learning networks can be employed that are fast and efficient and also compute hidden features automatically.

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Face image quality assessment based on photometric features and classification techniques

Cited by 3 publications

References 11 publications

Efficient storage and retrieval of high-quality face images for effective face recognition

Efficient storage and retrieval of high-quality face images for effective face recognition

An Efficient Method for Face Quality Assessment on the Edge

Digital Image Classification Techniques

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