Abstract
Deep learning has rapidly been filtrating many aspects of human lives. In particular, image recognition by convolutional neural networks has inspired numerous studies in this area. Hardware and software technologies as well as large quantities of data have contributed to the drastic development of the field. However, the application of deep learning is often hindered by the need for big data and the laborious manual annotation thereof. To experience deep learning using the data compiled by us, we collected 2429 constrained headshot images of 277 volunteers. The collection of face photographs is challenging in terms of protecting personal information; we therefore established an online procedure in which both the informed consent and image data could be obtained. We did not collect personal information, but issued agreement numbers to deal with withdrawal requests. Gender and smile labels were manually and subjectively annotated only from the appearances, and final labels were determined by majority among our team members. Rotated, trimmed, resolution-reduced, decolorized, and matrix-formed data were allowed to be publicly released. Moreover, simplified feature vectors for data sciences were released. We performed gender and smile recognition by building convolutional neural networks based on the Inception V3 model with pre-trained ImageNet data to demonstrate the usefulness of our dataset.
Highlights
Deep learning has rapidly been filtrating many aspects of human lives
Deep learning is often viewed as having been catapulted into fame by the significant achievements of AlexNet at the ImageNet Large Scale Visual Recognition Challenge in 2 0121
We were interested in determining how accurately common deep learning methods could recognize the gender without individual bearings; that is, by using constrained photographs
Summary
Deep learning has rapidly been filtrating many aspects of human lives. In particular, image recognition by convolutional neural networks has inspired numerous studies in this area. We performed gender and smile recognition by building convolutional neural networks based on the Inception V3 model with pre-trained ImageNet data to demonstrate the usefulness of our dataset. Numerous facial datasets, such as F ERET7, JAFFE8, Eigenflow Based Face Authentication (EBFA)9, LFW10, Adience[11], IMDB-WIKI12, AFAD13, UTKFace[14], AIST Face201715, and Flickr-Faces-HQ (FFHQ)[16], have been developed and released. The majority of these datasets imposed challenging tasks when using images of an unconstrained nature. The copyright usually belongs to the original owners, and not to the research group Collecting such headshots with agreement from each subject is a formidable task involving the management of personal information. In contrast to unconstrained images, constrained images may be analyzed to understand the unrecognized soft biometric traits of human faces
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
