Lip Recognition for Authentication and Security

Sangve, Sunil; Mule, Nilakshi

doi:10.9790/0661-16371823

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…In 2014, Sangve and Mule not only used colour spaces for lip segmentation like the aforementioned researchers, but also began person recognition based on the lips [7]. They did this by combining the region of interest (ROI) with the appearance-based model (ABM), active shape model (ASM), and point distribution model (PDM).…”

Section: Literature Reviewmentioning

confidence: 99%

“…These include, and are not limited to, automatic lip-reading applications [1], speech recognition systems, which deal with the video signal as well as the audio (AV speech recognition) [2], visual passwords systems [3], face recognition systems [4], facial expression recognition [5], and human-computer interface [6]. Moreover, lips alone can be used for personal authentication as a standalone biometric system [7].…”

Section: Introductionmentioning

confidence: 99%

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Colour-based lips segmentation method using artificial neural networks

Hassanat

Alkasassbeh

Al-Awadi

et al. 2015

2015 6th International Conference on Information and Communication Systems (ICICS)

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A colour-based technique for lip segmentation is presented throughout this work. Basically, we make use of colour spaces to categorize pixels as either lip or non-lip using artificial neural networks. This study clearly shows how a novel method for fusion of the existing colour spaces practically produces better results than individual colour spaces. More accuracy in dealing with face images under many different conditions has been achieved. Comparing this work with other researchers' work using the same databases, we found that our method, which involves the fusion of different colour information that comes from different colour models, outperforms other methods.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Colour-based lips segmentation method using artificial neural networks

Hassanat

Alkasassbeh

Al-Awadi

et al. 2015

2015 6th International Conference on Information and Communication Systems (ICICS)

View full text Add to dashboard Cite

show abstract

“…fingerprint, iris, face, hand-geometry, etc. ), [4][5][6][7][8] signalbased (e.g. voice, electrocardiography [ECG], electroencephalography [EEG], photoplethysmography [PPG], etc.…”

Section: Introductionmentioning

confidence: 99%

Biosignal classification for human identification based on convolutional neural networks

Siam

Sedik

El‐Shafai

et al. 2021

Int J Communication

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Human identification is considered as a serious challenge for several applications such as cybersecurity and access control. Recently, the trend of human identification has been directed to human biometrics, which can be used to recognize persons based on some physiological or behavioral characteristics that they own, such as fingerprint, iris, and biosignals. There are several types of human biosignals including electroencephalography (EEG), electrocardiography (ECG), and photoplethysmography (PPG) signals. This paper presents a human identification system based on PPG signals. The proposed system consists of three main phases: signal acquisition, signal pre‐processing, and feature extraction/classification. The pre‐processing phase involves denoising of the acquired signal, transformation of the 1D signal sequence into a 2D image, and computation of the spectrogram. Feature extraction is carried out on the images obtained from the pre‐processing phase. Features are extracted from the images based on convolutional neural networks (CNNs). The proposed CNN model consists of a sequence of convolutional (CNV) and pooling layers. Finally, the obtained feature maps are fed to the classifier to discriminate human identities. The proposed identification algorithm is applied on signals with and without an additive white Gaussian noise (AWGN). The simulation results reveal that the proposed algorithm achieves an accuracy of 99.5% with the spectrogram representation and 89.8% with the 2D image representation, in the absence of noise. In addition, the paper gives a discussion of the efficiency of denoising techniques such as wavelet denoising, Savitzky–Golay and Kalman filtering, when involved with the proposed algorithm. The simulation results prove that the wavelet dencoising technique has a best performance among the discussed noise reduction techniques.

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Lip-Based Identification Using YOLOR

Farrukh

Haar

2022

Pattern Recognition and Artificial Intelligence

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Lip Recognition for Authentication and Security

Cited by 6 publications

References 3 publications

Colour-based lips segmentation method using artificial neural networks

Colour-based lips segmentation method using artificial neural networks

Biosignal classification for human identification based on convolutional neural networks

Lip-Based Identification Using YOLOR

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