A study on partial face recognition of eye region

Teo, Chuan Chin; Neo, Han Foon; Teoh, Andrew Beng Jin

doi:10.1109/icmv.2007.4469271

Cited by 3 publications

(6 citation statements)

References 10 publications

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“…[ Teo et al, 2007] used parts of frontal face images such as eye, nose, and mouth for personal authentication, the frontal human eye images were generated from Essex dataset with 153 subjects, the partial face images were tested with non-negative matrix factorization (NMF), local NMF (LNMF) and spatially confined NMF (SFNMF), Their experimental results showed that the LNMF performed better achieving 95.12% recognition rate. [Akbar et al, 2015] proposed an arithmetical model for face recognition, where he investigated several feature extraction methods such as discrete wavelet transform (DWT), discrete sine transform (DST), local binary patterns (LBP) and local phase quantization (LPQ), with several classifiers such as SVM, probabilistic neural network (PNN) and k-nearest neighbors (kNN).…”

Section: Literature Reviewmentioning

confidence: 99%

DeepVeil: deep learning for identification of face, gender, expression recognition under veiled conditions

Prasath¹,

Alkhazi²,

Alghamdi³

et al. 2022

IJBM

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Biometric recognition based on the full face is an extensive research area. However, using only partially visible faces, such as in the case of veiledpersons, is a challenging task. Deep convolutional neural network (CNN) is used in this work to extract the features from veiled-person face images. We found that the sixth and the seventh fully connected layers, FC6 and FC7 respectively, in the structure of the VGG19 network provide robust features with each of these two layers containing 4096 features. The main objective of this work is to test the ability of deep learning based automated computer system to identify not only persons, but also to perform recognition of gender, age, and facial expressions such as eye smile. Our experimental results indicate that we obtain high accuracy for all the tasks. The best recorded accuracy values are up to 99.95% for identifying persons, 99.9% for gender recognition, 99.9% for age recognition and 80.9% for facial expression (eye smile) recognition.

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Section: Literature Reviewmentioning

confidence: 99%

DeepVeil: deep learning for identification of face, gender, expression recognition under veiled conditions

Prasath¹,

Alkhazi²,

Alghamdi³

et al. 2022

IJBM

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“…; x n are non-overlapping facial regions such as eyes, nose, etc. There exists a unique solution k to the above equation with k [ À 1; k 6 ¼ 0 (Sugeno 1974). The values g i are known and are called densities of the fuzzy measure.…”

Section: Interpretation Of the Fuzzy Measurementioning

confidence: 99%

“…The second property (monotonicity) quantifies the psychologically motivated observation that the likelihood of a proper identification of the individual increases when the knowledge about the available region of face is augmented by pieces of knowledge concerning other facial areas. In the original definition of the fuzzy measure the limit condition is also provided (Sugeno 1974), lim n!1 g A n ð Þ ¼ g lim n!1 A n ð Þ ; where A n f g; n ¼ 1; 2; . .…”

Section: Interpretation Of the Fuzzy Measurementioning

confidence: 99%

“…In such a case a combination of two or more classifiers should be more efficient. If the set of facial regions is chosen, it is obvious that the Sugeno measure cannot be both sub-additive for one group of parts of face and super-additive for another because of the constant value of the parameter k. The parameter k can be determined by solving a polynomial equation of the following form (Sugeno 1974):…”

Section: Interpretation Of the Fuzzy Measurementioning

confidence: 99%

“…; is an arbitrary increasing sequence of measurable sets. Sugeno (1974) proposed a parametric version of the fuzzy measure (often denoted g k Þ by introducing the following aggregation scheme.…”

Section: Interpretation Of the Fuzzy Measurementioning

confidence: 99%

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A study in facial regions saliency: a fuzzy measure approach

et al. 2013

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People recognize familiar faces in a similar way by using interior facial features (facial regions) such as eyes, nose, mouth, etc. However, the importance of these regions in the realization of face identification and a quantification of the impact of such regions on the recognition process could vary from one region to another. An intuitively appealing observation is that of monotonicity: the more regions are taken into account in the recognition process, the better. From a formal point of view, the relevance of the facial regions and an aggregation of these pieces of experimental evidence can be described in the formal setting of fuzzy measures. Fuzzy measures are of particular interest with this regard given their monotonicity property (which stands in a clear contrast with the more restrictive additivity property inherent to probability-like measures). In this study, we concentrate on the construction of fuzzy measures (more specifically, k-fuzzy measure) and characterize their performance in the problem of face recognition using a collection of experimental data.

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Eye-Strip based Person Identification based on Non-Subsampled Contourlet Transform

Patil¹,

Kothari²,

Bhurchandi³

2015

IJCA

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Many state-of-the-art face recognition systems fail to identify a person when most portions of the face are occluded. This paper addresses an intriguing problem of face recognition only with eye-strip samples as testing images and full images or again eye-strips as database images. Non-sub-sampled Contourlet transform is a distinguished algorithm for extracting soft and smooth contour-like edges without any loss of information. It also produces eminent features due to its localization and directionality preserving abilities and has strong resemblance with abilities of human visual cortex to extract features. This does not require any boosting of subband coefficients. We have proposed a novel approach that adds all the sub-bands at each pyramidal level of nonsubsampled contourlet transform to achieve a hybrid high frequency composite sub-band and minimize the dimensionality followed by feature extraction using Weber Local Descriptor (WLD) on the hybrid high frequency subband. Linear Discriminant Analysis is further used for insignificant feature reduction followed by nearest neighbor matching using Euclidean distance measure. JAFFE, Yale and Faces94 Essex university databases are used for experimentation and benchmarking. The analysis indicates that the proposed approach yields a robust feature vector and very good recognition rates using only eye-strip features.

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A study on partial face recognition of eye region

Cited by 3 publications

References 10 publications

DeepVeil: deep learning for identification of face, gender, expression recognition under veiled conditions

DeepVeil: deep learning for identification of face, gender, expression recognition under veiled conditions

A study in facial regions saliency: a fuzzy measure approach

Eye-Strip based Person Identification based on Non-Subsampled Contourlet Transform

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