## 4 Emerging Types of Biometric De-Identification
### Emotion-Based De-Identification  
**Definition 13**. : _Emotion-based de-identification: Emotion-based biometric de-identification can be defined as the introduction of perturbation in emotion to obfuscate either traditional or auxiliary biometric traits or both of them._  
Emotions are one of the most common auxiliary data that are frequently extracted from a human face; however, they can also be deduced from gait and speech [81]. For instance, the authors of [82] proposed a novel method to de-identify faces and the soft biometrics while retaining emotions. They highlighted the difference between their proposed method and naive approaches, such as blurring, pixelization, blindfolding, and inversion of the face images. Their adaptive filtering algorithm smoothed the facial details until the software-based authentication rate fell to approximately half of the original and the human recognition rate. Thus, the authors of [40] masked original faces with donor-faces to de-identify an image of the original subject. The results show that emotions such as disgust, surprise, and neutrality are preserved 100% of the time, while anger and sadness are preserved more than 98% of the time. Lastly, fear and happiness are preserved only 79% of the time. Similarly, other works used Generative Neural Networks (GNNs) to mask original faces by using donor faces while preserving emotion [35]. The above research studies aimed to preserve emotion while concealing identities. A dual problem of concealing emotion while preserving identity is also possible for consideration. The authors of [83] used Cycle Generative Adversarial Networks (Cycle GANs) to transform a person's voice to hide emotions while retaining the ability for personal identification and speech recognition. Another less common parameter that can be estimated from a face is the body mass of a person [84]. Biometrics such as gait, Electroencephalogram (EEG), and Electrocardiography (ECG) are also gaining popularity for the emotion recognition problem and being researched for personal identification [72, 79, 85]. Since recognition methods involving these biometric traits are not studied as extensively as facial biometrics, experiments aimed at de-identification of these biometric traits have rarely been conducted. The particular biometric features that play a vital role in person identification are still uncertain; hence, not many have attempted to leverage those features. In [86], features responsible for human activity recognition were compared by using different machine learning methods. In [74], novel techniques for identifying the most significant gait features for emotion recognition were proposed. Such works can be extended to learn important features required for \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors** & **Year** & **Biometrics** & **De-Identified Biometrics** & **Unchanged Biometrics** & **Accuracy of Recognition** \\ \hline \hline \multirow{3}{*}{[PERSON] et al. [71]} & \multirow{3}{*}{2012} & \multirow{3}{*}{Gait} & \multirow{3}{*}{None} & \multirow{3}{*}{All biometrics} & 24.23\% prediction error for gender estimation and 5.39 mean absolute error for age estimation \\  & & & & & 94.0\% on gait recognition \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of sensor-based identification and de-dentification methods. gait-based person identification. Therefore, the features exclusively important for identification can be suppressed to achieve de-identification. Recently, many works attempt to identify person age from their biometrics. Notably, a recent attempt based on gait is presented in [80]. De-identifying age while preserving gait can be a new direction of research. Table 6 demonstrates the works that were performed on emotion-based identification and de-identification.