Silhouette-based human identification from body shape and gait

By Robert T. Collins, Ralph Gross, Jianbo Shi. Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition, 2002

583 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/686d07173fd8280536fdc4e358aa240f3443d649

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Gait Components and Their Application to Gender Recognition (2008, background): “These large variations are not found in conventional bio-metrics, e.g., we cannot move the eyes below the nose—in other words, the relative positions of the eyes and nose on the face can vary only over a small range.”
A survey on visual surveillance of object motion and behaviors (2004, methodology): “These methods generally consider motion as a whole to characterize its spatio-temporal distributions [27], [58], [59], [61], [62], [177], [178], [185].”
Silhouette Analysis-Based Gait Recognition for Human Identification (2003, methodology): “In summary, Comparison 1 shows that the proposed method outperforms [11], [22], [36] and is comparable to [34] in recognition rate, but is the lowest in computational cost.”
The humanID gait challenge problem: data sets, performance, and analysis (2005, background): “It is only recently that human identification (HumanID ) from gait has received attention and become an active area of computer vision [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29].”
Recognizing action at a distance (2003, background): “Another class of methods analyze motion periodicity [10, 15, 5, 4].”
A review of biometric technology along with trends and prospects (2014, unknown): “strides, length, cadence, speed, singularity of silhouette shape [237], SVM [238], DTW [239, 240], HMM [241-244], VHT [245, 246], Radon transform [247], LPP [248, 249], DLA [250], Wavelets [251] CMU Mobo, UMD, USF”
Individual recognition using gait energy image (2006, methodology): “(Legends: USF - direct frame shape matching [15]; DGEI - direct GEI matching, this paper; CMU - key frame shape matching [5]; SPS1/SPS2 clustered frame shape matching with two criteria [18]; SGEI - statistical GEI feature matching, this paper.”
Automatic gait recognition based on statistical shape analysis (2003, methodology): “computational cost of [26] and [47] was relatively higher than”
Gait recognition: a challenging signal processing technology for biometric identification (2005, background): “DIMENSIONALITY REDUCTION A natural question arises in the context of gait analysis: How much information do we need to extract from a gait sequence in order to capture most discriminative information? On the temporal axis, it appears that shape information can be captured using four or five characte”
Fusion of static and dynamic body biometrics for gait recognition (2003, unknown): “Current gait recognition approaches may be explicitly classified into two major categories, namely model-based methods [2,4,10] and motion-based methods [7,8,11,12].”
Improved gait recognition by gait dynamics normalization (2006, background): “The average of the rates reported in [8], [11], [12], [7], [13], [6], [14], [15], [16], [17], [18], [3] are plotted for different conditions. verification scenario, one is interested in deciding whether a given probe matches a hypothesized or claimed gallery identity, i.e., one-to-one match.”
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition (2007, methodology): “Currently, there are several successful representation models such as appearance-based models [6], [16], [19], [22], [29], [30], [34], [37], [40], stochastic statistical models [19], articulated biomechanics models [36], [3], [1], [7] in which a set of parameters describes the gait, and other…”