Multimodal biometric recognition using human ear and palmprint

Hezil, Nabil; Boukrouche, Abdelhani

doi:10.1049/iet-bmt.2016.0072

Cited by 90 publications

(43 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Hezil and Boukrouche () have fused two biometric modalities, namely, ear and palmprint, using BSIF texture descriptor at feature‐level fusion, attaining recognition rate of 100% using IIT Delhi‐2 ear and IIT Delhi palmprint database. On the other hand, a local feature extraction technique called speed‐up robust feature was used in Rathore, Prakash, and Gupta () to extract features from the enhanced images of ear and profile face.…”

Section: Related Workmentioning

confidence: 99%

On the use of ear and profile faces for distinguishing identical twins and nontwins

2019

View full text Add to dashboard Cite

This study aims to measure the efficiency of ear and profile face in distinguishing identical twins under identification and verification modes. In addition, to distinguish identical twins by ear and profile face separately, we propose to fuse these traits with all possible binary combinations of left ear, left profile face, right ear, and right profile face. Fusion is implemented by score‐level fusion and decision‐level fusion techniques in the proposed method. Additionally, feature‐level fusion is used for comparison. All experiments in this paper are also implemented on nontwins individuals, and the recognition performance of twins and nontwins are compared. Local binary patterns, local phase quantization, and binarized statistical image features approaches are used as texture‐based descriptors for feature extraction process. Images under controlled and uncontrolled lighting are tested. Ear and profile images from ND‐TWINS‐2009‐2010 dataset are used in the experiments. The experimental results show that the proposed method is more accurate and reliable than using ear or profile face images separately. The performance of the proposed method for recognizing identical twins as recognition rate is 100% and 99.45%, and equal error rates are 0.54% and 1.63% in controlled and uncontrolled illumination conditions, respectively.

show abstract

Section: Related Workmentioning

confidence: 99%

On the use of ear and profile faces for distinguishing identical twins and nontwins

2019

View full text Add to dashboard Cite

show abstract

“…Examples of the behavioral traits include Voice, Signature, Gait, and Keystroke [2][3][4][5], and [6]. On the other hand, Physiological traits include Iris, Retina, Face, Ear, DNA, Hand Geometry, Palm, and Fingerprint [7][8][9][10][11][12]. Speaker recognition is a form of behavioral biometrics which is used to verify an individual's claimed identity from his or her voice.…”

Section: Introductionmentioning

confidence: 99%

Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database

Al-Kaltakchi

Taha

Shehab

et al. 2020

IJEECS

View full text Add to dashboard Cite

<p><span lang="EN-GB">In this paper, different feature extraction and feature normalization methods are investigated for speaker recognition. With a view to give a good representation of acoustic speech signals, Power Normalized Cepstral Coefficients (PNCCs) and Mel Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction. Then, to mitigate the effect of linear channel, Cepstral Mean-Variance Normalization (CMVN) and feature warping are utilized. The current paper investigates Text-independent speaker identification system by using 16 coefficients from both the MFCCs and PNCCs features. Eight different speakers are selected from the GRID-Audiovisual database with two females and six males. The speakers are modeled using the coupling between the Universal Background Model and Gaussian Mixture Models (GMM-UBM) in order to get a fast scoring technique and better performance. The system shows 100% in terms of speaker identification accuracy. The results illustrated that PNCCs features have better performance compared to the MFCCs features to identify females compared to male speakers. Furthermore, feature wrapping reported better performance compared to the CMVN method. </span></p>

show abstract

“…Local binary patterns, Weber local descriptor, and binary statistical image features of ear and palm image were used to discriminate the features for person identification [11]. Individual classifier is used for iris, finger vein and fingerprint then it is integrated through optimal score level fusion technique with 98.4% accuracy [12]. Feature encryption based multimodal biometric authentication of fingerprint, finger vein, and retina are used for authentication.…”

Section: Introductionmentioning

confidence: 99%

Dimension Reduction of Hand and Face Feature Level Fusion in Multimodal Biometric Authentication

Gowda¹,

AN²

2019

IJITEE

View full text Add to dashboard Cite

The proposed work is a multimodal biometric authentication approach with image texture feature dimension reduction of trained feature vector which leads reduction in memory size and in turn reduces the computational time. In this paper hand and face features are used for person identification. The texture features of hand image are extracted using Haar and several Daubechie’s of 2D-DWT followed by 2D- edge detector gives better identification with reduction in feature vector and face features are extracted by neighborhood common characterization with block based segmentation approach to estimate the disparity in face. The neighborhood common characterization based structure recognition with a person representative per sample is more effective. The neighborhood features are constructed by extracting the similar blocks in the image, the intra pixel disparity feature is obtained by exploiting external common images to estimate the feasible facial disparities. Neighborhood common characterization reduces the overall residual of the given features over the local feature, common disparity dictionary, and shape based residual of a block. Neighbourhood common characterization representation, of face recognize with one representative per person more effectively. The system uses either of the biometric traits for person identification with 99.98% of authentication rate.

show abstract

Multimodal biometric recognition using human ear and palmprint

Cited by 90 publications

References 33 publications

On the use of ear and profile faces for distinguishing identical twins and nontwins

On the use of ear and profile faces for distinguishing identical twins and nontwins

Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database

Dimension Reduction of Hand and Face Feature Level Fusion in Multimodal Biometric Authentication

Contact Info

Product

Resources

About