Me

My Academic Contributions and Publications

Patent Summary

Dr. Farshid Pirahansiah 3 Patents - AI Innovations in Vehicle Detection, Facial Analysis, and Image Augmentation

This collection of patents presents cutting-edge innovations in computer vision and facial analysis, enhancing applications in surveillance, advertising, and vehicle detection.

The first patent, A Method for Augmenting a Plurality of Face Images (WO 2021/060971 A1), leverages Generative Adversarial Networks (GAN) to augment face images from surveillance videos, creating realistic face images from multiple angles to enhance recognition accuracy.

The second patent, System for Providing Advertisement Contents Based on Facial Analysis (WO 2020/141969 A2), utilizes facial recognition technology to dynamically adjust digital advertisements based on the analysis of facial features, improving engagement while safeguarding user privacy.

The third patent, A Method for Detecting a Moving Vehicle (WO 2021/107761 A1), introduces an advanced image processing system that captures and enhances video footage to accurately detect moving vehicles, even under challenging lighting conditions, using noise filtering and edge enhancement techniques.

Together, these patents demonstrate innovative applications of deep learning, facial analysis, and image processing to solve real-world challenges in traffic surveillance, targeted advertising, and facial recognition.

WO 2021/060971 A1 - A Method for Augmenting a Plurality of Face Images

Title: Augmenting Face Images for Video Surveillance
Subtitle: Enhancing Surveillance with GAN-Based Face Augmentation
Short Description: This patent presents a method for augmenting face images in surveillance systems using Generative Adversarial Networks (GAN) to generate realistic face images from multiple viewpoints.
Hashtags: #FaceAugmentation #GAN #Surveillance

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Highlights:

Summary:
This patent offers a method for augmenting face images in video surveillance systems using Generative Adversarial Networks (GANs). The system captures face images from various viewpoints, augments them through data transformations, and generates high-quality face images using GANs. The augmented images are then selected based on quality for training deep learning models. This system significantly improves face recognition accuracy, especially in environments where capturing full facial details is difficult.

WO 2020/141969 A2 - System for Providing Advertisement Contents Based on Facial Analysis

Title: Targeted Digital Advertising via Facial Recognition
Subtitle: Enhancing Digital Ad Engagement Through Facial Analysis
Short Description: This patent covers a system that uses facial recognition to dynamically adjust digital advertisements based on a user’s facial features.
Hashtags: #DigitalAdvertising #FacialRecognition #TargetedAds

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Highlights:

Summary:
This patent describes a system for providing more engaging digital advertisements by analyzing users’ facial features using deep learning models. The system identifies key features such as age, gender, and emotions to display tailored ad content on digital signage. This enhances the relevance of displayed content and increases the likelihood of engagement. It is designed to improve marketing efficiency without directly collecting personal data.

WO 2021/107761 A1 - A Method for Detecting a Moving Vehicle

Title: Advanced Vehicle Detection in Surveillance
Subtitle: Enhancing Vehicle Detection via Image Processing
Short Description: This patent describes a system that detects moving vehicles by enhancing images from video streams through edge detection and noise filtering techniques.
Hashtags: #VehicleDetection #ImageProcessing #TrafficSurveillance

Main Points:

Highlights:

Summary:
This patent introduces an innovative method for detecting moving vehicles in surveillance footage by utilizing advanced image processing techniques. It outlines processes such as illumination enhancement, Sobel edge detection, and a sophisticated method to close open edges and identify vehicles based on homogenous body properties. It is particularly adept at handling noise in the image using geometric filtering and relational analysis, improving the accuracy of vehicle detection even in low-light conditions.

Visual Language Models (VLM)

As an experienced director and team leader, I excel in scaling operations, expanding teams, and creating a flexible, collaborative work culture. I bring proven expertise in team building and leadership, with a hands-on approach that adapts to any role or challenge. Self-motivated and highly organized, I can independently manage projects from inception to completion, ensuring continuous progress through swift issue resolution.

I’m adept at translating complex technical concepts for non-technical stakeholders and investors, enabling me to effectively pitch ideas, secure funding, and drive growth. With a keen eye for opportunities and process optimization, I have a track record of scaling teams and organizations while maintaining operational excellence. My leadership style empowers cross-functional teams, fosters innovation, and aligns efforts toward shared goals. Thriving in dynamic environments, I’m dedicated to building sustainable, high-performing teams that achieve long-term success.

Experienced in Full Stack LLM development, including model training, fine-tuning, deployment, and integrating LLMs with scalable applications and APIs.