Photographs of flat impressions and their projective symmetry

An unusual case of cranial image recognition

We encountered an unusual practical case of craniofacial identification recently. The peculiarity of the case was that the skull itself was not available for examination unlike other such common cases. The supplied material exhibits were, a nearly front view photograph of a skeletonized face and a front view face photograph of the suspected victim. Further, the condition of the skull during taking its photograph was such that its lower and the upper jaws were not in a normal closed condition. The procedure involved in dealing with such a complicated craniofacial identification problem would be quite interesting from a forensic investigator's point of view, since standard methods of skull identification like photo/video superimposition techniques were not at all applicable here. As such, the present case report provides the details of the multiphase procedure adapted by us in dealing with this abnormal case. A solution to this unprecedented craniofacial identification problem was worked out by appropriate exploration of a newly introduced digital image processing technique that is based on craniofacial symmetry perception. The procedure leads to the reconstruction of a superimposable cranial image with upper and lower teeth in normal closed condition for establishment of its identity in usual way.

An economised craniofacial identification system

It has been attempted to develop an economised craniofacial identification system, as a special automated version of photo/video superimposition technique, that can deal with common cases of personal identification with the aid of a skull and a nearly front view face photograph of the suspected victim. The proposed method is economic in respect of (i) cost of hardware configuration, (ii) processing time as well as (iii) manual labour involved. Over and above, it has got a capability to take care of ambiguities due to soft tissue thickness during the selection of facial features, which is a part of the procedure. In order to reconstruct a 2-D cranial image, superimposable over the facial one, the new method does not need any reconstruction of a digitised 3-D cranial image. It works simply by a suitable segment-wise processing of a 2-D cranial image with the aid of the symmetry perceiving adaptive neuronet (SPAN), that has recently been introduced in connection with nearly front view facial image recognition. The final comparison of the facial and the superimposable cranial images is as versatile as the same for facial image recognition by SPAN.A practical application of this extended version of SPAN has been demonstrated in the present paper.

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.

Photographs of the human face and broken projective symmetry

The exact projective symmetry manifested in photographs is broken if the object is not flat. It has been found that front view perspectives of the human face, photographed with limited variations in camera angle (within 30 degrees) show a breaking projective symmetry sufficiently small to be considered for accurate comparison. As a practical implementation of this new approach, a computer-controlled video camera superimposition technique has been developed and demonstrated in the identification of a disguised human face.