A symmetry perceiving adaptive neural network and facial image recognition.
The paper deals with the forensic problem of comparing nearly from view and facial images for personal identification. The human recognition process for such problems, is primarily based on both holistic as well as feature-wise symmetry perception aided by subjective analysis for detecting ill-defined features. It has been attempted to approach the modelling of such a process by designing a robust symmetry perceiving adaptive neural network. The pair of images to be compared should be presented to the proposed neural network (NN) as source (input) and target images. The NN learns about the symmetry between the pair of images by analysing examples of associated feature pairs belonging to the source and the target images. In order to prepare a paired example of associated features for training purpose, when we select one particular feature on the source image as a unique pixel, we must associate it with the corresponding feature on the target image also. But, in practice, it is not always possible to fix the latter feature also as a unique pixel due to pictorial ambiguity. The robust or fault tolerant NN takes care of such a situation and allows fixing the associated target feature as a rectangular array of pixels, rather than fixing it as a unique pixel, which is pretty difficult to be done with certainty. From such a pair of sets of associated features, the NN searches out proper locations of the target features from the sets of ambiguous target features by a fuzzy analysis during its learning. If any of target features, searched out by the NN, lies outside the prespecified zone, the training of the NN is unsuccessful. This amounts to non-existence of symmetry between the pair of images and confirms non-identity. In case of a successful training, the NN gets adapted with appropriate symmetry relation between the pair of images and when the source image is input to the trained NN, it responds by outputting a processed source image which is superimposable over the target images and identity may subsequently be established by examining detailed matching in machine-made superimposed/composite images which are also suitable for presentation before the court. The performance of the proposed NN has been tested with various cases including simulated ones and it is hoped to serve as a working tool of forensic anthropologists.
State Forensic Science Laboratory, Calcutta, India.
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Sinha
D005145:Face; D018732:Forensic Anthropology / Q000379:methods; D005556:Form Perception; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D014430:Twins, Monozygotic