You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Detecting Faces in Images : A Survey 
Chapter Title:  Benchmark Test Sets for Face Detection 
1. Abstract of Cited Paper (BIB001):  We present a neural network-based upright frontal face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We present a straightforward procedure for aligning positive face examples for training. To collect negative examples, we use a bootstrap algorithm, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting nonface training examples, which must be chosen to span the entire space of nonface images. Simple heuristics, such as using the fact that faces rarely overlap in images, can further improve the accuracy. Comparisons with several other state-of-the-art face detection systems are presented, showing that our system has comparable performance in terms of detection and false-positive rates. 
2. Abstract of Cited Paper (BIB002):  In this paper, we present a neural network-based face detection system. Unlike similar systems which are limited to detecting upright, frontal faces, this system detects faces at any degree of rotation in the image plane. The system employs multiple networks; a "router" network first processes each input window to determine its orientation and then uses this information to prepare the window for one or more "detector" networks. We present the training methods for both types of networks. We also perform sensitivity analysis on the networks, and present empirical results on a large test set. Finally, we present preliminary results for detecting faces rotated out of the image plane, such as profiles and semi-profiles. 
3. Abstract of Cited Paper (BIB003):  This paper describes a face image database which has been created and developed at Kodak as a common database for direct benchmarking of automatic face detection and recognition algorithms. This consumer application-oriented face image database is composed of two main sub-databases, one for face detection, and one for face recognition. The database is intended to be distributed to researchers both inside and outside of Kodak working in face detection and recognition research. The database contains pictures taken using consumer digital cameras, scanned in from a photo scanner, as well as pictures from Kodak Image Magic picture disks. The details of the face database such as the development process and the image file formats, are described, together with a discussion on some application scenarios, as well as current benchmarking activities. 
4. Abstract of Cited Paper (BIB004):  In this paper, we describe a statistical method for 3D object detection. We represent the statistics of both object appearance and "non-object" appearance using a product of histograms. Each histogram represents the joint statistics of a subset of wavelet coefficients and their position on the object. Our approach is to use many such histograms representing a wide variety of visual attributes. Using this method, we have developed the first algorithm that can reliably detect human faces with out-of-plane rotation and the first algorithm that can reliably detect passenger cars over a wide range of viewpoints.

Literature Review:
The abovementioned databases are designed mainly to measure performance of face recognition methods and, thus, each image contains only one individual. Therefore, such databases can be best utilized as training sets rather than test sets. The tacit reason for comparing classifiers on test sets is that these data sets represent problems that systems might face in the real world and that superior performance on these benchmarks may translate to superior performance on other real-world tasks. Toward this end, researchers have compiled a wide collection of data sets from a wide variety of images. Sung and Poggio created two databases for face detection  , BIB001 . The first set consists of 301 frontal and nearfrontal mugshots of 71 different people. These images are high quality digitized images with a fair amount of lighting variation. The second set consists of 23 images with a total of 149 face patterns. Most of these images have complex background with faces taking up only a small amount of the total image area. The most widely-used face detection database has been created by Rowley et al. BIB002 ,  (available at http://www.cs.cmu.edu/~har/faces.html). It consists of 130 images with a total of 507 frontal faces. This data set includes 23 images of the second data set used by Sung and Poggio BIB001 . Most images contain more than one face on a cluttered background and, so, this is a good test set to assess algorithms which detect upright frontal faces. Fig. 12 shows some images in the data set collected by Sung and Fig. 12 . Sample images in Sung and Poggio's data set BIB001 . Some images are scanned from newspapers and, thus, have low resolution. Though most faces in the images are upright and frontal. Some faces in the images appear in different pose. Poggio BIB001 , and Fig. 13 shows images from the data set collected by Rowley et al. BIB002 . Rowley et al. also compiled another database of images for detecting 2D faces with frontal pose and rotation in image plane BIB003 . It contains 50 images with a total of 223 faces, of which 210 are at angles of more than 10 degrees. Fig. 14 shows some rotated images in this data set. To measure the performance of detection methods on faces with profile views, Schneiderman and Kanade gathered a set of 208 images where each image contains faces with facial BIB002 . Some images contain hand-drawn cartoon faces. Most images contain more than one face and the face size varies significantly. expressions and in profile views BIB005 . Fig. 15 shows some images in the test set. Recently, Kodak compiled an image database as a common test bed for direct benchmarking of face detection and recognition algorithms BIB004 . Their database has 300 digital photos that are captured in a variety of resolutions and face size ranges from as small as 13 Â 13 pixels to as large as 300 Â 300 pixels. Table 3 summarizes the characteristics of the abovementioned test sets for face detection.