We then introduce the concept of recognition certainty. Certainty involves comparing the distance a face has from its correct match in the database to the distance it has from other members in the database. Evidently, our recognition output is more reliable (or has better certainty) if a face is much closer to its match and than it is to other members of the database. Consider face#0 from the database which is shown with its synthesized mug-shot image in Figure . The eyes, nose and mouth have been localized accurately at positions i0', i1', i2' and i3'. These anchor points are used to generate a mug-shot version of face#0 using Equation .
This face is a member of our database (face0) and so d(probe,face0)=d(face0,face0)=0. However, if we perturb the values i0', i1', i2' and i3' by a small amount, the resulting probe image using Equation will be different and dmin will no longer be 0. The distance of the probe face (probe) to the correct match face#0 in the database is defined as d(probe,face0). The distance to the closest incorrect response is , as defined in equation :
In Equation we compute a margin of safety which is positive when the probe resembles its correct match face#0 and negative when the probe matches another element of the database, instead.
Since the probe is actually the result of the function N in Equation , it has i0, i1, i2 and i3 as its parameters as well. By the same token, the value of c in Equation also has i0, i1, i2 and i3 as parameters. Thus, it is more appropriate to write c(i0x,i0y,i1x,i1y,i2x,i2y,i3x,i3y). However, for compactness, we shall only refer to the certainty as c.
We shall now compute the sensitivity of the certainty (c) to variations in the localization (i0, i1, i2 and i3) for the image Icorresponding to face#0. This is done by computing .
We vary the values which cause the localization of a feature point to move around its original position. The anchor point's displacement caused by a particularly large is depicted in Figure . The dimensions of this image are and the intra-ocular distance is approximately 60 pixels. In the experiments, the values are varied over a range of [-15,15] pixels each. We then synthesize a new mug-shot image from the perturbed anchor points.
Figure shows several synthesized images after perturbing and , with all other values fixed at 0. Not surprisingly, the mug-shots that are synthesized appear slightly different depending on the position of i0 (the locus of the left eye). Similarly, Figure shows the approximations after the KL encoding of the mug-shots in Figure . The approximations, too, are affected, showing that the KL transformations is sensitive to errors in localization. Finally, in Figure we show the value of c as we vary and , with all other .
Figure shows the value of c for variations in the (x,y)position of the right eye anchor point. Similarly, Figure and Figure show the same analysis for the nose point under two different views. Finally, Figure shows the effect of perturbing the mouth point. This surface is quite different from the ones in the previous experiments. In fact, the value of c stays constant and positive indicating that the changes in the mouth position have brought no change in the synthesized mug-shot and that the recognition performance is unaffected by mouth localization errors. This is due to the insensitivity the 3D normalization procedure (defined in Chapter 4) has to the locus of the mouth point.
The above plots show that the localization does not have to be perfect for recognition to remain successful. In these graphs, as long as the value of cis positive, then the subject is identifiable. Even though these sensitivity measurements were made by varying only two parameters, the 8 dimensional sensitivity surface can be approximated by a weighted sum of the 4 individual surfaces described above. Thus, an 8 dimensional sub-space exists which defines the range of the 8 perturbations that will be tolerated before recognition errors occur. In short, the anchor-point localizations may be perturbed by several pixels before recognition degrades excessively.
From the above plots, it is evident that the (the nose locus) is the most sensitive anchor point since it causes the most drastic change in c. Consequently, an effort should be made to change the normalization algorithm to reduce the sensitivity of the recognition to this locus. On the other hand, there is a large insensitivity to the location of the mouth. This is due to the limited effect the mouth has in the normalization procedure. In fact, the mouth only determines the vertical stretch or deformation that needs to be applied to the 3D model. Thus, an effort should be made to use the location of the mouth more actively in the normalization algorithm discussed in Chapter 4. Recall that an error Emouth was present in the normalization while the other 3 anchor points always aligned perfectly to the eyes and nose in the image (using the WP3P). Thus the 3D normalization has an alignment error concentrated upon the mouth-point which is the only point on the 3D model which does not always line-up with its destination on the image. If this error could be distributed equally among all four features points, each point will be slightly misaligned and the total misalignment error would be less. Consequently, the 3D model's alignment to the face in the image would be more accurate, overall. Thus, we would attempt to minimize Etotal as in Equation instead of minimizing Emouthwith Eleft-eye=Eright-eye=Enose=0. The end result would be an overall greater insensitivity and recognition robustness for the 8 localization parameters combined.
We have thus presented the system's structure as a whole. The localization and recognition tests evaluate its performance. For one training image, the system is competitive when compared to contemporary face recognition algorithms such as the one proposed by Lawrence. Other current algorithms include  and  which report recognition rates of 98% and 92% respectively. However, these algorithms were tested on mostly frontal images (not the Achermann database or similar database). Finally, a sensitivity analysis depicts the dependence of the recognition module on the localization output. We see that the localization does not have to be exact for recognition to be successful. However, the sensitivity plots do show that recognition is not equally sensitive to perturbations in the localization of different anchor point. Thus, the normalization process needs to be adjusted to compensate for this discrepancy.