Combined face and gait recognition using alpha matte preprocessing
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Citations
Relative distance features for gait recognition with Kinect
Improved Gait Recognition using Gradient Histogram Energy Image
Gait-based person identification by spectral, cepstral and energy-related audio features
Exploiting gradient histograms for gait-based person identification
Image Matching Algorithm based on Feature-point and DAISY Descriptor
References
Robust Real-Time Face Detection
Robust real-time face detection
Adaptive background mixture models for real-time tracking
A Closed-Form Solution to Natural Image Matting
Individual recognition using gait energy image
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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.