Browsing by Author "Kiazadeh, Mahsa"
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- Assessing the existence of visual clues of human ovulationPublication . Kiazadeh, Mahsa; Gonçalves, Gabriela; Shahbazkia, Hamid RezaIs the concealed human ovulation a myth? The author of this work tries to answer the above question by using a medium-size database of facial images specially created and tagged. Analyzing possible facial modifications during the mensal period is a formal tool to assess the veracity about the concealed ovulation. In normal view, the human ovulation remains concealed. In other words, there is no visible external sign of the mensal period in humans. These external signs are very much visible in many animals such as baboons, dogs or elephants. Some are visual (baboons) and others are biochemical (dogs). Insects use pheromones and other animals can use sounds to inform the partners of their fertility period. The objective is not just to study the visual female ovulation signs but also to understand and explain automatic image processing methods which could be used to extract precise landmarks from the facial pictures. This could later be applied to the studies about the fluctuant asymmetry. The field of fluctuant asymmetry is a growing field in evolutionary biology but cannot be easily developed because of the necessary time to manually extract the landmarks. In this work we have tried to see if any perceptible sign is present in human face during the ovulation and how we can detect formal changes, if any, in face appearance during the mensal period. We have taken photography from 50 girls for 32 days. Each day we took many photos of each girl. At the end we chose a set of 30 photos per girl representing the whole mensal cycle. From these photos 600 were chosen to be manually tagged for verification issues. The photos were organized in a rating software to allow human raters to watch and choose the two best looking pictures for each girl. These results were then checked to highlight the relation between chosen photos and ovulation period in the cycle. Results were indicating that in fact there are some clues in the face of human which could eventually give a hint about their ovulation. Later, different automatic landmark detection methods were applied to the pictures to highlight possible modifications in the face during the period. Although the precision of the tested methods, are far from being perfect, the comparison of these measurements to the state of art indexes of beauty shows a slight modification of the face towards a prettier face during the ovulation. The automatic methods tested were Active Appearance Model (AAM), the neural deep learning and the regression trees. It was observed that for this kind of applications the best method was the regression trees. Future work has to be conducted to firmly confirm these data, number of human raters should be augmented, and a proper learning data base should be developed to allow a learning process specific to this problematic. We also think that low level image processing will be necessary to achieve the final precision which could reveal more details of possible changes in human faces.
- A comprehensive comparative study of quick invariant signature (QIS), dynamic time warping (DTW), and hybrid QIS + DTW for time series analysisPublication . Shahbazkia, Hamid Reza; Khosravani, Hamid Reza; Pulatov, Alisher; Hajimani, Elmira; Kiazadeh, MahsaThis study presents a comprehensive evaluation of the quick invariant signature (QIS), dynamic time warping (DTW), and a novel hybrid QIS + DTW approach for time series analysis. QIS, a translation and rotation invariant shape descriptor, and DTW, a widely used alignment technique, were tested individually and in combination across various datasets, including ECG5000, seismic data, and synthetic signals. Our hybrid method was designed to embed the structural representation of the QIS with the temporal alignment capabilities of DTW. This hybrid method achieved a performance of up to 93% classification accuracy on ECG5000, outperforming DTW alone (86%) and a standard MLP classifier in noisy or low-data conditions. These findings confirm that integrating structural invariance (QIS) with temporal alignment (DTW) yields superior robustness to noise and time compression artifacts. We recommend adopting hybrid QIS + DTW, particularly for applications in biomedical signal monitoring and earthquake detection, where real-time analysis and minimal labeled data are critical. The proposed hybrid approach does not require extensive training, making it suitable for resource-constrained scenarios.