毕业设计(论文)
外文资料翻译
原文:
EVALUATION OF STATE-OF-THE-ART ALGORITHMS FOR REMOTE FACE
RECOGNITION
Abstract
In this paper, we describe a remote face database which has been acquired in an unconstrained outdoor environment. The face images in this database suffer from variations due to blur, poor illumination, pose, and occlusion. It is well known that many state-of-the-art still image-based face recognition algorithms work well, when constrained (frontal, well illuminated, high-resolution, sharp, and complete) face images are presented. In this paper, we evaluate the effectiveness of a subset of existing still image-based face recognition algorithms for the remote face data set. We demonstrate that in addition to applying a good classification algorithm, consistent detection of faces with fewer false alarms and finding features that are robust to variations mentioned above are very important for remote face recognition. Also setting up a comprehensive metric to evaluate the quality of face images is necessary in order to reject images that are of low quality.
Keywords Remote, Face Recognition.
- INTRODUCTION
During the past two decades, face recognition (FR) has received great attention and tremendous progress has been made. Currently, most of the FR algorithms are applied to databases which are collected at close range (less than a few meters) and under different levels of controlled environments, such as in CMU PIE [1], FRGC/FRVT [2], FERET [3] data sets. Yet, in many scenarios in real life applications, we cannot control the acquisition of face images; the images we get can suffer from poor illumination, blur, occlusion etc. which are great challenges to current FR algorithms. In [4], Yao et al. describe a face video database, UTK-LRHM, acquired from long distances and with high magnifications. They address the magnification blur to be the major degradation. Huang et al. [5] presented a database named ”Labeled Faces in the Wild” (LFW) which has been collected from the web. Although it has ”natural” variations in pose, lighting, expression, etc., there is no guarantee that such a set accurately captures the range of variation found in the real world [6]. Besides, most objects in LFW only have one or two images which may be not enough to evaluate different FR experiments.
In order to study and develop more robust algorithms for FR, we have put together a remote face database in which a significant number of images are taken from long distances and under unconstrained outdoor environments. The quality of the images differs in the following aspects: the illumination is not controlled and is often pretty bad in extreme conditions; there are pose variations and faces are also occluded as the subjects are not cooperative [7]; finally, the effects of scattering [7] and high magnification resulting from long distance contribute to the blurriness of face images. We manually cropped and labeled the face images according to different illumination conditions (good, bad and really bad), pose(frontal and non-frontal), blur or no-blur etc in a systematic way so that users can conveniently select the desired images for their experiments.
We evaluated two state-of-the-art FR algorithms on this remote face database including a baseline algorithm and the recently developed algorithm based on sparse representation[8]. Based on our limited experiments using the remote face data set, we make the following observations: detection of faces and subsequent extraction of robust features is as important as the recognition algorithms that are used. The performance of recognition algorithms improves gradually as the number of gallery images increases. The recognition accuracy varies from low thirties to mid nineties depending on the quality of images and the number of available gallery images. It is important to design a quality metric so that face images that have low quality can be rejected.
The organization of this paper is as follows; In Section 2, we describe the remote face database collected by the authorsrsquo; group. Section 3 briefly describes the algorithms that are evaluated and corresponding recognition results. Finally conclusions are given in Section 4.
2. REMOTE FACE DATABASE DESCRIPTION
The distance from which the face images were taken varies from 5m to 250m under different scenarios. Since we could not reliably extract all the faces in the data set using existing
state-of-the-art face detection algorithms and the faces only occupied small regions in large background scenes, we manually cropped the faces and rescaled to a fixed size. The resulting database for still color face images contains 17 different individuals and 2106 face images in total. The number of faces per subject varies from 48 to 307. All images are 120 by 120 pixel png images. Most faces are in frontal poses.
We manually labeled the faces according to different illumination conditions, occlusion, blur and so on. In total, the database contains 688 clear images, 85 partially occluded images, 37 severely occluded images, 540 images with medium blur, 245 with sever blur, and 244 in poor illumination condition. The remaining images have two or more conditions, such as poor lighting and blur, occlusion and blur etc. These face images are not used in the following experiments. Figure 1 shows some sample images from the database: These face images show large variations, some of which are not easily recognizable even for humans.
Fig.
剩余内容已隐藏,支付完成后下载完整资料
资料编号:[254140],资料为PDF文档或Word文档,PDF文档可免费转换为Word
以上是毕业论文外文翻译,课题毕业论文、任务书、文献综述、开题报告、程序设计、图纸设计等资料可联系客服协助查找。