Convolutional neural networks for gender prediction from smartphone‐based ocular images

Rattani, Ajita; Reddy, Narsi; Derakhshani, Reza

doi:10.1049/iet-bmt.2017.0171

Cited by 42 publications

(38 citation statements)

References 33 publications

(74 reference statements)

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“…However, mobile biometrics face more challenges, since smartphones usually have less computing capability and limited energy. Therefore, light-weight secure algorithm design for mobile biometrics is an emerging research topic [82][83][84]. iii.…”

Section: Discussionmentioning

confidence: 99%

Security and Accuracy of Fingerprint-Based Biometrics: A Review

et al. 2019

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Biometric systems are increasingly replacing traditional password-and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper. recognition as an example. In the stage of enrollment, a user presents their finger to the fingerprint sensor and a fingerprint image is acquired by the sensor module. Certain features of the acquired fingerprint image are extracted, and further adapted or transformed to generate template data for the purpose of comparison in the verification stage. In the verification stage, the fingerprint image of a query is collected by the sensor module. The feature representations of the query fingerprint image go through the same process as in the enrollment stage, so as to obtain query data. The query data are then compared with the template data so that a matching outcome is attained.

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Section: Discussionmentioning

confidence: 99%

Security and Accuracy of Fingerprint-Based Biometrics: A Review

et al. 2019

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“…Some ocular attributes, such as pupil position and radius, have also been used for user profiling in [ 62 , 63 ]. In these cases, CNN were utilized to predict the age and gender of different users.…”

Section: User Profilingmentioning

confidence: 99%

Privacy-Preserving Sensor-Based Continuous Authentication and User Profiling: A Review

Hernández–Álvarez

Fuentes

González-Manzano

et al. 2020

Sensors

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Ensuring the confidentiality of private data stored in our technological devices is a fundamental aspect for protecting our personal and professional information. Authentication procedures are among the main methods used to achieve this protection and, typically, are implemented only when accessing the device. Nevertheless, in many occasions it is necessary to carry out user authentication in a continuous manner to guarantee an allowed use of the device while protecting authentication data. In this work, we first review the state of the art of Continuous Authentication (CA), User Profiling (UP), and related biometric databases. Secondly, we summarize the privacy-preserving methods employed to protect the security of sensor-based data used to conduct user authentication, and some practical examples of their utilization. The analysis of the literature of these topics reveals the importance of sensor-based data to protect personal and professional information, as well as the need for exploring a combination of more biometric features with privacy-preserving approaches.

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“…A new algorithm called "Ensemble Margin Instance Selection" (EMIS), based on Random Forest, is proposed in [21], to select the most informative data to optimize the classification of white blood cells. Finally, [22] proposes the use of a convolutional neural network to detect the gender (male or female) of a person based on a photograph of their eyes taken with the front camera of a smartphone, in everyday conditions with a normal camera. For the above, image processing and artificial intelligence could be used for bruise dating.…”

Section: Rel Ated Work Smentioning

confidence: 99%

Bruise dating using deep learning

Tirado

Mauricio

2020

Journal of Forensic Sciences

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According to the latest report of the World Health Organization (WHO) [1], domestic violence against women is a global scourge, with a prevalence of physical or sexual abuse against women of just over 30% globally and 29.8% in Latin America (see Figure 1). Some victims of violence report these facts to the authorities, which require forensic clinical services such as bruise dating to support complaints, since the results of dating, used as evidence, can be decisive for justice. However, many of these complaints are filed due to lack of evidence or error in the dating of bruise, which aggravates the consequences of this scourge. Bruise, also known as ecchymosis, is the internal bleeding of the skin due to the rupture of blood vessels, which is caused by an impact with an edgeless object, without tearing or cutting the skin. Therefore, blood escapes from blood vessels close to the surface of the skin being trapped under it [2]. This generates a coloration on the surface of the skin, which shows a series of colors that vary over time and can be visible for up to 30 days from its appearance, so the medical literature reports the use of color scales for dating of an bruise. This procedure consists in determining the age of this bruise; thus, the bruise is visualized in situ or through photographs, and the judgment of an expert is applied [3], who, in general, is a coroner, and its importance lies in being evidence fundamental in a trial for domestic violence or against women. There are studies on the dating of bruise, from the medical and forensic point of view, where the use of histological analysis [4], genetic [5], chromatography [6], and visual inspection techniques are

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Convolutional neural networks for gender prediction from smartphone‐based ocular images

Cited by 42 publications

References 33 publications

Security and Accuracy of Fingerprint-Based Biometrics: A Review

Security and Accuracy of Fingerprint-Based Biometrics: A Review

Privacy-Preserving Sensor-Based Continuous Authentication and User Profiling: A Review

Bruise dating using deep learning

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