SELM: Siamese extreme learning machine with application to face biometrics

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s00521-022-07100-zExtreme learning machine (ELM) is a powerful classification method and is very competitive among existing classification methods. It is speedy at training. Nevertheless, it cannot perform face verification tasks properly because face verification tasks require the comparison of facial images of two individuals simultaneously and decide whether the two faces identify the same person. The ELM structure was not designed to feed two input data streams simultaneously. Thus, in 2-input scenarios, ELM methods are typically applied using concatenated inputs. However, this setup consumes two times more computational resources, and it is not optimized for recognition tasks where learning a separable distance metric is critical. For these reasons, we propose and develop a Siamese extreme learning machine (SELM). SELM was designed to be fed with two data streams in parallel simultaneously. It utilizes a dual-stream Siamese condition in the extra Siamese layer to transform the data before passing it to the hidden layer. Moreover, we propose a Gender-Ethnicity-dependent triplet feature exclusively trained on various specific demographic groups. This feature enables learning and extracting useful facial features of each group. Experiments were conducted to evaluate and compare the performances of SELM, ELM, and deep convolutional neural network (DCNN). The experimental results showed that the proposed feature could perform correct classification at 97:87% accuracy and 99:45% area under the curve (AUC). They also showed that using SELM in conjunction with the proposed feature provided 98:31% accuracy and 99:72% AUC. SELM outperformed the robust performances over the well-known DCNN and ELM methods.This work was supported by the Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang and projects BIBECA (RTI2018-101248-B-I00 MINECO/FEDER) and BBforTAI (PID2021-127641OB-I00 MICINN/FEDER)

The 17 activity classes in the MUV dataset.

<p>The entries are ranked in decreasing order of average mean pairwise similarity across four fingerprints.</p

Relative improvement/worsening with respect to similarity searching for top 1% retrieved–average across ten runs, 16 similarity coefficients, and four fingerprints.

<p>Relative improvement/worsening with respect to similarity searching for top 1% retrieved–average across ten runs, 16 similarity coefficients, and four fingerprints.</p

Molecules retrieved by different methods in top 1% of the ranked database for activity class I17.

<p>Molecules retrieved by different methods in top 1% of the ranked database for activity class I17.</p

Ranks assigned to the performances of 6 classifiers by 17 activity classes from Table 7.

<p>Ranks assigned to the performances of 6 classifiers by 17 activity classes from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195478#pone.0195478.t007" target="_blank">Table 7</a>.</p

Maximum percentage actives retrieved in top 1% of ranked database using similarity searching technique (average across 10 runs).

<p>Bold face is the best result in each activity class.</p

Enrichment plot for the top 1% of the sorted library for each performer with ECFP_6 fingerprint on activity class I01.

<p>Enrichment plot for the top 1% of the sorted library for each performer with ECFP_6 fingerprint on activity class I01.</p

The percentage hit rate in the top 1% of the ranked database retrieved by WS-ELM and CWS-ELM in conjunction with Jaccard/Tanimoto (JT) and Sokal/Sneath(1) (SN1).

<p>Figures in bold face represent the best performance.</p

Maximum percentage of active molecules retrieved in the top 1% with WS-ELM and similarity searching in 17 activity classes–averaged across ten runs, 16 similarity coefficients, and four fingerprints.

<p>Maximum percentage of active molecules retrieved in the top 1% with WS-ELM and similarity searching in 17 activity classes–averaged across ten runs, 16 similarity coefficients, and four fingerprints.</p

Early recognition criteria suggested by [35, 38].

<p>(Left) EF (Right) Ratio of true positive rate to the false positive rate, at 0.5%, 1.0%, 2.0%, and 5.0% of the ranked database for WS-ELM and its variants, SVM, RF, and Similarity Searching (SS). Each bar represents the mean value across all activity classes and ten runs.</p