Combined face and gait recognition using alpha matte preprocessing
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Citations
The TUM Gait from Audio, Image and Depth (GAID) database
Robust gait recognition: a comprehensive survey
The Fall of One, the Rise of Many: A Survey on Multi-Biometric Fusion Methods
Human Identification From Freestyle Walks Using Posture-Based Gait Feature
Relative distance features for gait recognition with Kinect
References
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
The humanID gait challenge problem: data sets, performance, and analysis
Recognizing Friends by Their Walk ; Gait Perception without Familiarity Cues
A Closed Form Solution to Natural Image Matting
Identification of humans using gait
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Frequently Asked Questions (8)
Q2. What future works have the authors mentioned in the paper "Combined face and gait recognition using alpha matte preprocessing" ?
For future work, stronger and better face and gait methods should be combined. It can be foreseen that recognition rates could improve even further.
Q3. What is the advantage of the splitting of the test sequences?
The splitting of the test sequences has the advantage, that for each sequence, multiple sub faces of each person can be used for classification.
Q4. What is the main advantage of behavior based features over other physiologic features?
A major advantage of these behavior based features over other physiologic features is the possibility to identify people from large distances and without the person’s direct cooperation.
Q5. Why is there a band on the silhouette?
due to the nature of the image capturing, there is a band on the silhouette which belongs partially to foreground and partially to background.
Q6. What is the dimensional transformation matrix obtained using MDA?
These (c− 1) dimensional vectors zk are obtained as followszk = Umdayk, k = 1, . . . , N (4)where Umda is the transformation matrix obtained using MDA.
Q7. What is the d′ d dimensional PCA space?
Then the projection to the d′ < d dimensional PCA space is given byyk = Upca(gk − g), k = 1, . . . , N (3) Here Upca is the d′×d transformation matrix with the first d′ orthonormal basis vectors obtained using PCA on the training set {g1, g2, . . . , gN} and g = ∑N k=1 gk is the mean of the training set.
Q8. What is the way to recognize a face?
Even though face recognition has its performance peak at high resolution frontal face images, it can still be seen that facial profile recognition can contribute to the performance, when combined correctly.