TARIFA PARA SEGUROS A CORTO PLAZO

Where N is the total number of samples. The score is bounded between -1 for incorrect clustering and +1 for highly dense clustering. Scores around zero indicate overlapping clusters. This metric has the advantage that the score is higher when clusters are dense and well separated, which relates to a standard concept of a cluster.

4.3

Experimental Results

This section describes all the experiments carried on during the development of this work with different features extractors (Openface, VGG-face and VGG-face-finetuned), different clustering algorithms (rank-order, HAC-SL, HAC-CL, HAC-WD, HAC-AG, and k-means) and different face alignment methods (point, mass and crop). Table 4.1shows which parameters were used to test different algorithms.

Algorithm Parameter Start End Step

Rank-Order k 5 150 5

t 0.05 3 0.05

HAC h 0.1 5 0.01

kMeans k 2 20 2

Table 4.1. Values for different parameters and algorithms tested during the development of this work. The step is the increment value from start to end (both inclusive). For instance, for rank-order: parameter k was tested with 5, 10, 15, and so on until 150.

4.3.1

LFW Dataset Experiments

Using: Rank-Order Clustering Algorithm

Table4.2shows the results when running the rank-order clustering algorithm on the entire LFW dataset. Each sub-table uses three different techniques for face alignment and two different features extractor. The first column shows the results using features extracted with the Openface DCNN, while the second column shows the results using features extracted with the VGG-face DCNN. Lastly, the third column shows the results using our VGG-face fine-tuned model.

4.3 Experimental Results 26

Alignment Openface VGG-face VGG-face-finetuned Point 0.290 0.444 0.555

Mass 0.009 0.684 0.738

Crop 0.062 0.729 0.679

Table 4.2. Comparison of the F1score of the Rank-Order clustering algorithm on LFW dataset. In bold the best result within each DCNN. In blue the best overall result.

As we can see, in table4.2, the best result among the three face alignment techniques using Openface features is given by the Point technique because this DCNN was trained using this face alignment technique, and thus, if we align faces in different way, the extracted features will be slightly different. In the other hand, using VGG-face features, the best result is given by the Crop technique. This is, because this DCNN was trained without any kind of face alignment, the face images were only cropped. Thus, the reason of why the other techniques fail to give better result, is because of the clustering algorithm itself. In the last column of table4.2is given the result of our fine-tuned VGG-face network. As we can see, it gives the overall result in this clustering algorithm using Mass face alignment technique.

To Summarize, the combination of VGG-face-finetuned features and Mass technique gives the better result (0.738), in comparison to the other feature extractor and other face alignments, for this clustering algorithm. We will use this result for further comparisons in next sections.

Using: HAC Clustering Algorithm

Table4.3shows the results when running the HAC clustering algorithm on the entire LFW dataset. As in previous comparison, we use three different techniques for face alignment and two different feature extractor. To use this clustering algorithm, we must specify a linkage strategy (see3.3). In our experiments, we use the following methods: Single link (SL), Complete link (CL), Ward (WD) and Average (AG) (see3.3for more detail). Table

4.3ashows the result using features extracted with the Openface DCNN, while table4.3b

4.3 Experimental Results 27

Alignment SL CL WD AG Point 0.520 0.461 0.346 0.666 Mass 0.006 0.012 0.013 0.011 Crop 0.099 0.035 0.039 0.053

(a) Using Openface DCNN model.

Alignment SL CL WD AG Point 0.664 0.448 0.202 0.636 Mass 0.903 0.804 0.334 0.938

Crop 0.862 0.802 0.318 0.905

(b) Using VGG-face DCNN model.

Alignment SL CL WD AG

Point 0.6924 0.4108 0.2452 0.6935 Mass 0.8626 0.8278 0.3472 0.9179 Crop 0.9035 0.7026 0.3768 0.9227

(c) Using VGG-face-finetuned DCNN model.

Table 4.3. Comparison of the F1 score of the HAC clustering algorithm with different linkage criteria on LFW dataset. In bold the best result within each DCNN. In blue the best overall result.

As in previous section, the Point face alignment technique using Openface DCNN features extractor gives best result among the other techniques (as we can see in table4.3a). Besides, the Average linkage criteria yields best result among other criteria. This is due to the fact that Average criteria calculates its distance between two clusters using the mean distance between all points in both clusters. In the other hand, as we see in table4.3b, the Mass technique gives best result among other face alignment techniques. As stated in previous section, this is because the network was trained without any type of alignment. Indeed, Masstechnique gives better face alignment than the other two, because it takes into account several facial landmarks (see section3.1). Average linkage criteria still being the best in comparison to the other.

If we have a look at table4.3c, our fine-tuned model outperforms Openface model in all cases and it is able to outperform some VGG-face results. But the best overall result is done by VGG-face with a small difference from our fine-tuned model.

Thus, the combination of VGG-face features, Mass face alignment technique and Aver- age linkage criteria, yields to better result (0.938). We will use this result for further comparisons in next sections.

4.3 Experimental Results 28

RankOrder vs HAC

Comparing table4.2 and table4.3, we can see that using VGG-face as a face features extractor and HAC-Average as a clustering algorithm, clearly outperforms Openface as a face feature extractor and RankOrder as a clustering algorithm. Thus, to compare with state of the art results using this dataset, i.e. LFW, we will be using the aforementioned combination.

4.3.2

IJB-B Dataset Experiments

The result of the six (in total there are seven, but we were unable to execute the last one due to a lack of resources) experiments in this dataset are shown in table4.4. As in previous sections, we used both DCNN to extract face features in all the the experiments. In this case we are not using any sort of face alignment (we tested the same face alignments as in previous sections, but they yield to a very bad result). Thus we are using the Crop technique, which only crop the face specified by a bounding box (the bounding box is taken from the provided one along the dataset).

In all the experiments, the best result is given by the VGG-face DCNN and HAC-Average clustering algorithm. And as expected, with an exception of the firsts experiments, as the number of identities increases, the F1score decreases.

4.4 Comparison to State of the Art 29