# Sex Recognition through ECG Signals aiming toward Smartphone Authentication
###### Abstract  
Physiological signals are strongly related to a person's state of health and carry information about the human body. For example, by ECG, it is possible to obtain information about cardiac disease, emotions, personal identification, and the sex of a person, among others. This paper proposes the study of the heartbeat from a soft-biometric perspective to be applied to smartphone unlocking services. We employ the user heartbeat to classify the individual by sex (male, female) with the use of Deep Learning, reaching an accuracy of 94.4% \(\pm\) 2.0%. This result was obtained with the RGB representation of the union of the time-frequency transformation from the pseudo-orthogonal X, Y, and Z bipolar signals. Evaluating each bipolar contribution, we found that the XYZ combination provides the best category distinction using GoogleLeNet. The 24-h Holter database of the study contains 202 subjects with a female size of 49.5%. We propose an architecture for managing this signal that allows the use of a few samples to train the network. Due to the hidden nature of ECG, it does not present vulnerabilities like public trait exposition, light/noise sensibility, or learnability compared to fingerprint, facial, voice, or password verification methods. ECG may complement those gaps en route to a cooperative authentication ecosystem. sex recognition; ECG; user authentication; soft-biometrics; smartphone applications; machine learning +
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behaviors given the person's anatomical features. Although sex recognition has been covered by cardiologists, this area has turned to machine learning (ML) algorithms since 2014 [3; 19], and reciprocally, ECG has proven its biometric characteristics through ML, too [20]. In fact, biometric multimodal studies have pointed out that the inclusion of soft-biometric features can enrich and facilitate the discrimination performance [21], being mostly proven in camera-related systems. Soft-biometric refers to the extraction of shared--or nonunique--traits such as ethnicity, sex, age, etc., that help to describe human subjects but are not strong enough to distinguish between two people [22]. Following this scheme, we propose to treat the ECG sex property as a soft-biometric input for future inclusion in ECG-related verification engines to complement the classification space. Moreover, we intend to strengthen this technology to be adapted as a smartphone authentication tool in the near future. Recent tests show that smartphone fingerprint and face authentication hacking is possible [23; 24; 25; 26], evidencing that biometric authentication is not infallible [27]. Therefore, user privacy protection should be turned into an authentication community, embracing possible traits that could support the current open gates. In consequence, it is necessary to be aware of the strengths and weaknesses of authentication tools when the user context changes because each biometric method assumes the sensor's constraints. ECG biometrics' potential is not affected by light-saturated or noisy environments in contrast to touch-screen, camera, or microphone sensors. In addition, due to the face exposition or the fingerprint trace in daily touched objects, the copy reproduction attack is more probable for face and fingerprint. Instead, ECG is a hidden measure [3]. The nature of ECG includes an inherent liveness property as a prospective ubiquitous tool that could be used for continuous or transparent authentication mechanisms. Although this area is still maturing, dealing with issues such as signal time acquisition, computational complexity, and user comfortability, new related products evidence the first market inclusions as described in Section 5. The ECG fiducials orientation finds the temporal and amplitude inter- and intra-proportions of the P, Q, R, S, and T peaks (Figure 1) and has been a classical and useful tool for studying the ECG.