Periocular Biometrics for non-ideal images: with off-the-shelf Deep CNN &amp; Transfer Learning approach

Kumari, Punam; Seeja, K. R.

doi:10.1016/j.procs.2020.03.234

Cited by 18 publications

(15 citation statements)

References 29 publications

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“…Finally the user acceptance and the application's cost were also depended as factors of measuring the application or the biometric system. These factors were considered as deep learning factors and depended today in most of the fields that depend on recognition, detection inside the classification, and highly accurate results can be obtained in many papers when deep learning is used [11][12][13][14].…”

Section: Figure 1 Palm Used As a Part Of Handmentioning

confidence: 99%

“…This technique includes removing the unwanted parts of images and restructuring the parts that are eroded. Thus, two types of operations are included in morphological operation: erosion and dilation [11]. These two operations are done; the erosion is used for thinning the object, while the dilation is used for size increasing (Figure 4).…”

Section: Morphological Operationsmentioning

confidence: 99%

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Age-Dependent Palm Print Recognition Using Convolutional Neural Network

Alobaidy,

Yosif,

Alkababchi

2023

RIA

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Biometric engineering is one of the most important and modern fields that affect human life directly. It can be considered as a new technology relatively, that is used for identity verification and/or the identification of persons depending on their physiological features, which include the morphological, biological, and characteristics of their behaviors. Many types of biometric recognitions are used depending on features of eyes, faces, hands (palm and/or fingerprints), voice, and many others. All the works before were focused on persons' detection only but nor on their ages. This feature (age) considered as one of the not solved problems in the field of detection. In this paper, the palm recognition model consisted of many steps. The first step related to palm detection. Other techniques used to remove noisy portion from extracted image. After preparing images for training, a deep neural network represented by convolutional neural network is selected. A new idea and method (mechanism) is used. Palm print features' recognition algorithm depending on Convolutional Neural Network (CNN) is presented for recognizing individuals (persons recognition in different ages' classes). Palm print technique is depended for different ages' classes. The dataset is selected firstly for many known persons with different ages, for each person many palm image items are trained and tested using deep learning techniques. As mentioned, the CNN method is used for the training purpose, which means the recognition must be done depending on the CNN deep learning algorithm. The FAR and GAR factors are used to measure the performances of the recognition. The given results shown that the selection of the palm instead of other features types makes the recognition easier. More than 96% of the results were accurate. Also, the used algorithm which included the CNN had competitive performance, the algorithm succeeded to separate between the features according to the persons' ages. The overall process is completed within 0.01×10 -6 second, which can be considered fast and suggested to be used in real time.

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Section: Figure 1 Palm Used As a Part Of Handmentioning

confidence: 99%

Section: Morphological Operationsmentioning

confidence: 99%

Age-Dependent Palm Print Recognition Using Convolutional Neural Network

Alobaidy,

Yosif,

Alkababchi

2023

RIA

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“…With the emergence of deep learning approach, the focus of the researchers has been moved to learn robust representations by deep Convolutional Neural Networks (CNNs) for periocular recognition, achieving visible improvement in the performance of periocular biometric systems [17], [44], [45]. The semantics-assisted convolutional neural networks (SCNN) [44] was one of the first proposals that use deep learning-based representation for periocular images.…”

Section: B Periocular Face Recognitionmentioning

confidence: 99%

“…The results obtained show that these networks are able to outperform reference periocular features. Similarly, seven different off-the-shelf deep learning based CNN using transfer learning approach were implemented in [17] to analyse the utility of periocular region in non-ideal scenarios. A new method for masked face recognition was proposed in [20] by integrating a cropping-based approach with the Convolutional Block Attention Module (CBAM) to focus on the regions around eyes.…”

Section: B Periocular Face Recognitionmentioning

confidence: 99%

Towards Accurate and Lightweight Masked Face Recognition: An Experimental Evaluation

et al. 2022

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“…Despite of the heavy use of deep learning-based networks for periocular image matching in visible spectrum [ 18 , 27 , 41 ] the implementation of cross spectrum periocular matching (NIR to VIS or vice versa) is still a tough challenge. Ramaiah & Kumar [ 30 ] and Behera et al [ 4 ] did some studies on hand crafted features for cross spectral image matching and unfortunately, the results were not found satisfactory.…”

Section: Introductionmentioning

confidence: 99%

One shot learning approach for cross spectrum periocular verification

Kumari

Seeja

2023

Multimed Tools Appl

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The use of face mask during the COVID-19 pandemic has increased the popularity of the periocular biometrics in surveillance applications. Despite of the rapid advancements in this area, matching images over cross spectrum is still a challenging problem. Reason may be two-fold 1) variations in image illumination 2) small size of available data sets and/or class imbalance problem. This paper proposes Siamese architecture based convolutional neural networks which works on the concept of one-shot classification. In one shot classification, network requires a single training example from each class to train the complete model which may lead to reduce the need of large dataset as well as doesn’t matter whether the dataset is imbalance. The proposed architectures comprise of identical subnetworks with shared weights whose performance is assessed on three publicly available databases namely IMP, UTIRIS and PolyU with four different loss functions namely Binary cross entropy loss, Hinge loss, contrastive loss and Triplet loss. In order to mitigate the inherent illumination variations of cross spectrum images CLAHE was used to preprocess images. Extensive experiments show that the proposed Siamese CNN model with triplet loss function outperforms the states of the art periocular verification methods for cross, mono and multi spectral periocular image matching.

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Periocular Biometrics for non-ideal images: with off-the-shelf Deep CNN & Transfer Learning approach

Cited by 18 publications

References 29 publications

Age-Dependent Palm Print Recognition Using Convolutional Neural Network

Age-Dependent Palm Print Recognition Using Convolutional Neural Network

Towards Accurate and Lightweight Masked Face Recognition: An Experimental Evaluation

One shot learning approach for cross spectrum periocular verification

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