A Novel Feature Extraction Technique for Face Recognition

Mathew, Deepa; Devassia, V.P.; Thomas, Tessamma

doi:10.1109/iccima.2007.141

Cited by 10 publications

(3 citation statements)

References 15 publications

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“…In the first decade of the 21st century, studies have focused on feature-based approaches, and could possibly be separated into two distinct types: local appearancebased techniques that consider the facial image as a collection of discrete vectors with low dimensions and focus on crucial parts of the face like the nose, mouth, and eyes to create additional information and make face recognition easier. Local binary pattern (LBP) [22], histogram of oriented gradients (HOG) [23], correlation filters (joint transform correlator (JTC) [24], VanderLugt correlator (VLC) [25]) and discrete orthogonal moments (DOM) [26] are the most methods used in this sub category. In the second sub-category, keypointsbased techniques are utilized to detect particular geometric characteristics based on the geometry of the facial features (e.g., the space between the eyes, the circumference of the head) using algorithms like scale-invariant feature transform (SIFT) [27] and descriptors like speeded-up robust features (SURF) [28], binary robust independent elementary features (BRIEF) [29] and fast retina keypoint (FREAK) [30].…”

Section: Introductionmentioning

confidence: 99%

Robust face recognition using convolutional neural networks combined with Krawtchouk moments

Madmoune

Ouariachi

Zenkouar

et al. 2023

IJECE

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<span lang="EN-US">Face recognition is a challenging task due to the complexity of pose variations, occlusion and the variety of face expressions performed by distinct subjects. Thus, many features have been proposed, however each feature has its own drawbacks. Therefore, in this paper, we propose a robust model called Krawtchouk moments convolutional neural networks (KMCNN) for face recognition. Our model is divided into two main steps. Firstly, we use 2D discrete orthogonal Krawtchouk moments to represent features. Then, we fed it into convolutional neural networks (CNN) for classification. The main goal of the proposed approach is to improve the classification accuracy of noisy grayscale face images. In fact, Krawtchouk moments are less sensitive to noisy effects. Moreover, they can extract pertinent features from an image using only low orders. To investigate the robustness of the proposed approach, two types of noise (salt and pepper and speckle) are added to three datasets (YaleB extended, our database of faces (ORL), and a subset of <a name="_Hlk128818620"></a>labeled faces in the wild (LFW)). Experimental results show that KMCNN is flexible and performs significantly better than using just CNN or when we combine it with other discrete moments such as Tchebichef, Hahn, Racah moments in most densities of noises.</span>

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Section: Introductionmentioning

confidence: 99%

Robust face recognition using convolutional neural networks combined with Krawtchouk moments

Madmoune

Ouariachi

Zenkouar

et al. 2023

IJECE

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show abstract

“…Over the years, various algorithms and methods have been developed to recognize a face [3,4]. These approaches concentrate on outlining and extracting facial characteristics such as the eyes, nose, and mouth [5][6][7]. Though these algorithms attain excellent accuracy, there are significant hurdles when it comes to detecting faces.…”

Section: Introductionmentioning

confidence: 99%

“…Intrinsic factors include ageing and expressions [8][9][10], whereas external aspects include lighting, position, and so forth [11,12]. Nowadays, tracking criminals is a prominent use of face recognition applications [5][6][7][8][9][10]. This application is used for reducing the exponential increase in the crime rate, and also an effect shows predominant changes in the number of criminals.…”

Section: Introductionmentioning

confidence: 99%

Futuristic study of a criminal facial recognition system using open-source images

Kumar¹,

Gupta²

2023

Preprint

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Face recognition provides an uncertainty challenge to criminal face identification. Thus, this recognition has many uses, from security to entertainment. Even if other bio-metric characteristics like iris and fingerprints are reliable, a person must be present to use them. Therefore, they are not the best tool for thieves to use for authentication. Anyone can recognize a criminal in an image by using face recognition technology in conjunction with a criminal database that contains the individual's personal information. In addition to needing great accuracy, a criminal recognition system also needs to be flexible to numerous variations in illumination, occlusion, maturity level, postures, etc. We use the Haar cascade and Histograms of oriented gradients (HOG) recognition learning systems discussed and contrasted in this study. We assessed models and HOG outperformed the criminal recognition system. We also go through several practical uses for this criminal recognition.

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LBP Pattern-Processed Log-Gabor Filter for Expression and Illumination Robust Facial Recognition

Juneja

Rana

2019

Lecture Notes in Electrical Engineering

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A Novel Feature Extraction Technique for Face Recognition

Cited by 10 publications

References 15 publications

Robust face recognition using convolutional neural networks combined with Krawtchouk moments

Robust face recognition using convolutional neural networks combined with Krawtchouk moments

Futuristic study of a criminal facial recognition system using open-source images

LBP Pattern-Processed Log-Gabor Filter for Expression and Illumination Robust Facial Recognition

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