Identifying the best periocular region for biometric recognition

2017 ◽  
pp. 125-147
Author(s):  
David Jordan ◽  
Louise Mawn ◽  
Richard L. Anderson

Surgical Anatomy of the Ocular Adnexa is a beautifully and thoughtfully illustrated anatomical text that provides the ophthalmic surgeon or any surgeon working in the eyelid/orbital region with detailed yet concise, easy to read and understand descriptions of the anatomy in any particular region of the eyelid, orbit or nasolacrimal system. Throughout the text are clinical pearls and vignettes to help the reader appreciate why certain anatomical features are important to understand. Key anatomical concepts are highlighted and easy to visualize with real cadaver photos as well as the artists rendition of the same region. This book: - Develops a thorough understanding of the anatomy in the eyelid, orbit, nasolacriaml and periocular regions. - Fosters an appreciation of how knowledge of the anatomy leads to a better understanding of the pathophysiology of various disease processes involving the eyelid, orbit, nasolacrimal and periocular region. - Conveys the importance of anatomy in the surgical approach to various disease processes in the eyelid, orbit, nasolacrimal and periocular regions. This second edition will be an invaluable guidel to all those working in the eyelid, orbital, and nasolacrimal areas including residents, fellows and staff in ophthalmology, otolaryngology/head and neck surgery, plastic surgery and neurosurgeons working in and around the orbit.


Author(s):  
James Eric Mason ◽  
Issa Traore ◽  
Isaac Woungang

2021 ◽  
Vol 11 (13) ◽  
pp. 5880
Author(s):  
Paloma Tirado-Martin ◽  
Raul Sanchez-Reillo

Nowadays, Deep Learning tools have been widely applied in biometrics. Electrocardiogram (ECG) biometrics is not the exception. However, the algorithm performances rely heavily on a representative dataset for training. ECGs suffer constant temporal variations, and it is even more relevant to collect databases that can represent these conditions. Nonetheless, the restriction in database publications obstructs further research on this topic. This work was developed with the help of a database that represents potential scenarios in biometric recognition as data was acquired in different days, physical activities and positions. The classification was implemented with a Deep Learning network, BioECG, avoiding complex and time-consuming signal transformations. An exhaustive tuning was completed including variations in enrollment length, improving ECG verification for more complex and realistic biometric conditions. Finally, this work studied one-day and two-days enrollments and their effects. Two-days enrollments resulted in huge general improvements even when verification was accomplished with more unstable signals. EER was improved in 63% when including a change of position, up to almost 99% when visits were in a different day and up to 91% if the user experienced a heartbeat increase after exercise.


Author(s):  
Min Wang ◽  
Kathryn Kasmarik ◽  
Anastasios Bezerianos ◽  
Kay Chen Tan ◽  
Hussein Abbass

2021 ◽  
Vol 1900 (1) ◽  
pp. 012019
Author(s):  
Muhammad Muizz Mohd Nawawi ◽  
Khairul Azami Sidek ◽  
Alaa K Y Dafhalla ◽  
Amelia Wong Azman

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