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 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 recognition by building convolutional neural networks based on the Inception V3 model with pre-trained ImageNet data to demonstrate the usefulness of our dataset.

中文翻译：

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收集了277名志愿者的2429幅受限头像进行深度学习

深度学习已迅速过滤掉人类生活的许多方面。尤其是，通过卷积神经网络进行图像识别已经激发了该领域的许多研究。硬件和软件技术以及大量数据为该领域的蓬勃发展做出了贡献。但是，深度学习的应用通常由于需要大数据及其费力的手动注释而受到阻碍。为了使用我们收集的数据来体验深度学习，我们收集了277名志愿者的2429幅受限爆头图像。在保护个人信息方面，收集面部照片具有挑战性。我们建立了一个在线程序，可以在此程序中获得知情同意书和图像数据。我们没有收集个人信息，但发布了协议编号以处理提款请求。性别和微笑标签仅通过外观进行手动和主观注释，最终标签由我们团队成员中的多数决定。旋转，修剪，降低分辨率，脱色和矩阵形式的数据被允许公开发布。此外，还发布了用于数据科学的简化特征向量。我们通过基于Inception V3模型和预训练的ImageNet数据构建卷积神经网络来进行性别识别，以证明我们的数据集的实用性。并且允许将矩阵形式的数据公开发布。此外，还发布了用于数据科学的简化特征向量。我们通过基于Inception V3模型和预训练的ImageNet数据构建卷积神经网络来进行性别识别，以证明我们的数据集的实用性。并且允许将矩阵形式的数据公开发布。此外，还发布了用于数据科学的简化特征向量。我们通过基于Inception V3模型和预训练的ImageNet数据构建卷积神经网络来进行性别识别，以证明我们的数据集的实用性。