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

Hassanat, Ahmad B. A.; Albustanji, Abeer Ahmad; Tarawneh, Ahmad S.; Alrashidi, Malek; Alharbi, Hani; Alanazi, Mohammed M.; Alghamdi, Mansoor; Alkhazi, Ibrahim S.; Prasath, V.B. Surya

doi:10.1504/ijbm.2022.124683

Cited by 12 publications

(2 citation statements)

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“…Our future efforts will be focused on overcoming the limitations listed above: To improve accent learning, fluent speakers must be removed from the speech database, simply because there is no accent information preserved in their recordings. In order to overcome the effect of female voices, our future approach will divide the dataset into two, one for males and one for females, then train the deep learning method on each to produce two models, which are then merged to improve recognition results, as done by [49,50]. In addition to collecting more data to increase the size of the dataset for better deep learning.…”

Section: Discussionmentioning

confidence: 99%

Recognition of Arabic Accents From English Spoken Speech Using Deep Learning Approach

Habbash,

Mnasri,

Alghamdi

et al. 2024

IEEE Access

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Accents, or changes in how different people speak the same word/sentence in the same language, pose substantial communication issues in most spoken languages. This is a well-known fact, but how does the accent of one language affect learning/speaking another? In this paper, we look at how Arab accents influence the English language. To that end, we built a deep machine-learning system for Arabic accent recognition that was learned from an in-house English speech database of four Arabic accents collected from Jordan, Iraq, Saudi Arabia, and Tunisia. The proposed system employs Mel spectrograms of an English-spoken paragraph to train an LSTM neural network to recognize the accent in each sound signal. Although the collected data was extremely difficult to learn due to the presence of both males and females and fluent speakers in each class, the proposed system could recognize speakers with various accents by up to 79%. This answers the study's main question, demonstrating that speakers with an Arabic accent have their way of speaking English, which varies by country. As a result, if trained on appropriate and adequate data, the proposed system can also be used to recognize accents in any language.

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Section: Discussionmentioning

confidence: 99%

Recognition of Arabic Accents From English Spoken Speech Using Deep Learning Approach

Habbash,

Mnasri,

Alghamdi

et al. 2024

IEEE Access

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“…Another useful feature of smartphones is their capacity to support electronic payments, making mobile transactions and financial transactions more accessible [10]. Smartphones incorporate biometric authentication methods such as facial recognition [11], fingerprint identification [12,13], and palm-print identification [14,15], which are used not only for user recognition, but also for specialized applications such as identifying potential threats or terrorists [16,17].…”

Section: Introductionmentioning

confidence: 99%

Smartphone User Identification/Authentication Using Accelerometer and Gyroscope Data

Al-Mahadeen,

Alghamdi,

Tarawneh

et al. 2023

Sustainability

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With the increasing popularity of smartphones, user identification has become a critical component to ensure security and privacy. This study looked into how smartphone sensors’ data can be utilized to identify/authenticate users and gives suggestions for the best application components. A public smartphone dataset was used to train a deep learning algorithms, conventional classifiers, and voting classifiers, which were then used to identify the users. Feature selection and Pre-processing techniques were investigated to improve the performance. According to the results, Recursive Feature Elimination beat the other feature-selection approaches, and Long Short-Term Memory (LSTM) had the best identification performance, as evidenced by a relatively large number of machine learning performance metrics. Even with a larger number of users, the proposed identification system performed well and outperformed existing approaches, which were primarily designed and tested on the same public smartphone dataset. In terms of user authentication, this study compared the effectiveness of accelerometer data against gyroscope data. According to the findings, the accelerometer data surpassed the gyroscope data in the authentication process. Notably, the study revealed that employing LSTM to combine the accelerometer and gyroscope data resulted in near-perfect user authentication. The insights gained from this study help to develop user identification and authentication approaches that employ smartphone accelerometer data.

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Facial Emotion Recognition in-the-Wild Using Deep Neural Networks: A Comprehensive Review

Boughanem,

Ghazouani,

Barhoumi

2023

SN COMPUT. SCI.

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

Cited by 12 publications

References 0 publications

Recognition of Arabic Accents From English Spoken Speech Using Deep Learning Approach

Recognition of Arabic Accents From English Spoken Speech Using Deep Learning Approach

Smartphone User Identification/Authentication Using Accelerometer and Gyroscope Data

Facial Emotion Recognition in-the-Wild Using Deep Neural Networks: A Comprehensive Review

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