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: Class Energy Image Analysis for Video Sensor-Based Gait Recognition: A Review 
Chapter Title:  Conclusions and Comments for Further Research 
1. Abstract of Cited Paper (BIB001):  Traditional image representations are not suited to conventional classification methods such as the linear discriminant analysis (LDA) because of the undersample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two-dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA, compared with existing preprocessing methods such as the principal components analysis (PCA) and 2DLDA, include the following: 1) the USP is reduced in subsequent classification by, for example, LDA, 2) the discriminative information in the training tensors is preserved, and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, whereas that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor-function-based image decompositions for image understanding and object recognition, we develop three different Gabor-function-based image representations: 1) GaborD is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS, and GaborSD representations are applied to the problem of recognizing people from their averaged gait images. A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS, or GaborSD image representation, then using GDTA to extract features and, finally, using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the University of South Florida (USF) HumanID Database. Experimental comparisons are made with nine state-of-the-art classification methods in gait recognition.

Literature Review:
This paper has presented a comprehensive review of the video sensor-based gait representation methods, especially spatio-temporal motion summary approaches, namely Class Energy Image approaches. We have reviewed and analyzed various video-based Class Energy Image approaches, which have the following properties: (1) They contain rich motion information such as motion frequency, temporal and spatial changes of the human body; (2) They compress the information of a sequence to a template, which reduces the size of the gait database; (3) They are suitable for real time systems because Class Energy Image has a high computational efficiency; (4) They are insensitive to the quality of silhouettes and robust to silhouette errors or image noise. Based on the different ways of feature extraction and Class Energy Image generation, we divide the Class Energy Image approaches into three categories: the gait information accumulation approach, the gait information introduction approach and the gait information fusion approach. In summary, the gait information accumulation approach performs better and provides richer information than the original binary gait image. The disadvantage of this kind of method is that some useful information may be lost, and the problem of the inadequate training samples is raised; the gait information introduction approach can highlight dynamic information, meanwhile preserving the static information; the gait information fusion approach employs feature layer fusion and decision layer fusion method to achieve the fusion of static, dynamic and temporal information. Since the gait information fusion approach mainly takes different feature images into consideration, there is little correlation between different feature images. It is a promising direction to study the extraction of different features, and to fuse them using different fusion strategies. The experimental results demonstrated that some Class Energy Image approaches could attain higher recognition accuracy with good robustness and efficiency. Especially, the performance of CGHI is better than other templates. In addition, it is noteworthy that more studies should be implemented on the Class Energy Images with good performances, such as AEI, FDEI and CGI, etc. We note that while significant successes have been achieved in his domain of research, some more work needs to be done as indicated next. (1). As demonstrated in BIB002 , extracting Gabor features from the gait energy images can further improve the performance of gait recognition. It is interesting to study whether the other invariant descriptors, such as Local Binary Pattern (LBP)  and Histogram of Oriented Gradients (HOG) BIB001 which have been demonstrated to benefit visual information processing and recognition in general, can further enhance the performance for the Class Energy Image-based gait recognition. (2). The current gait information accumulation approach and gait information introduction approach highlight some gait information, but miss lots of information with discriminative faculties. Experimental results reveal that CGHI, which belongs to the gait information fusion approach, outperforms other Class Energy Images. The reason for this phenomenon is that CGHI contains richer information (dynamic, static and temporal information). This provides a new insight to gait recognition. We could derive more gait features by implementing feature-level fusion for different Class Energy Images. The gait features to be fused could be selected from the gait information accumulation approach and gait information introduction approach. (3). Gait is sensitive to various covariate conditions such as view angle, speed, clothing, carrying condition (backpack, briefcase, handbag, etc.), shoe-wear type, surface, accessories, injury, mood and to name a few. The further researches of Class Energy Image approach should take all these conditions into consideration and new approaches with robustness and efficiency should be presented. (4). The Class Energy Image approaches can also be applied to other biometric identification areas such as multi-pose face recognition. Several multi-pose face images from the same individual could be integrated to an image by using Class Energy Image approaches. In order to achieve the real-time performance, the representations of biometric features should be as simple as possible, and the computational complexity will be as low as possible.