CV — Dr. Farshid Pirahansiah

Pirahansiah, Farshid

By Dr. Farshid Pirahansiah — computer vision & embedded edge AI consultant, Berlin, Germany · 12+ years. Updated 2026-09-27.

Curriculum vitae covering 12+ years of computer vision R&D, 21 publications, and 3 patents.

As an AI and computer vision expert with 12+ years of experience collaborating with esteemed global organizations, my expertise encompasses AI research and development, machine learning, deep learning, Internet of Things (IoT), and model optimization for edge and cloud-based solutions. With a portfolio of 21 publications, three patents, and extensive practical experience in real-time computer vision applications, I have spearheaded groundbreaking projects in generative AI, video analytics, and intelligent systems. Proficient in C++, Python, OpenCV, and advanced GPU optimization, I am recognized for bridging the gap between cutting-edge research and commercially viable products.

Me

I have a total of 21 publications, including 3 patents, 2 book chapters, 6 journal articles, and 10 conference papers. My Google Scholar profile indicates 142 citations and an h-index of 8.
Dr. Farshid Pirahansiah CV
My podcast

My Academic Contributions and Publications

My Google Scholar Profile

Metric Value

Citations 142

h-index 8

i10-index 5
ScienceOpen Collection

My Publications ( Total 21 )

My Patents (3)

My Books (2)

My Journals (6)

My Conference Papers (10)

Hardware I Used and Can Work With

Axelera AI M2
Axelera Metis M.2 on Raspberry Pi 5
M.2 AI Inference Acceleration card: Axelera® AI’s M.2 card, powered by a single, quad-core Metis® AIPU, is perfect for constrained devices that require high-performance AI inference.
Hailo AI
The Hailo-15 SBC development kit : Hailo-15 AI Vision Processor
Hardware: a single board computer (SBC) with integrated Hailo-15, supporting sensor interfaces as well as data, memory, communication and display interfaces.
Software: a complete Yocto based Linux distribution, full-stack DSP libraries, BSP and drivers.
FPGA Xilinx: Kria KV260 Vision AI Starter Kit
Kria KV260 Vision AI Starter Kit: Zynq™ UltraScale+™ MPSoC, SOM + Carrier Card + Thermal Solution,
The development platform for AMD Kria™ K26 SOMs, the KV260 Starter Kit is built for advanced vision application development without requiring complex hardware design knowledge.
advance computer vision applications
Raspberry Pi 3,4,5
Edge computing, low-power processing
Intel Neural Compute Stick 2
Portable AI inference, accelerated deep learning
OpenCV AI Kit
Integrated AI vision processing, depth sensing
Google Coral (TPU)
On-device ML, low-latency AI processing
Nvidia Jetson Nano
AI at the edge, accelerated vision tasks
Nvidia GPU (RTX 1080 to 5090)
High-performance GPU computing, deep learning training

Platforms and Architectures

ARM
Low-power consumption, mobile CV applications
Apple Silicon
Efficient CV workflows, ML model optimization
x86-64
Versatile performance, large-scale CV model training

Operating Systems

Linux
Preferred for CV development, extensive libraries and tools
Windows
Compatibility with development environments, AI frameworks
MacOS
Optimized ML support, Apple-specific AI frameworks

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

Main Points: - Generates a variety of face images from video surveillance
- Uses data augmentation techniques and GAN models to create realistic images
- Captures face images from multiple angles, improving recognition accuracy
- Fuzzy logic module ensures image quality before training deep learning models

Highlights:
- GAN-based face augmentation ensures high-quality, diverse face images
- Addresses limitations in conventional surveillance systems by generating more detailed face images

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

Main Points: - Digital signage adjusts advertisement content based on user’s facial features
- Uses deep learning techniques (CNN, GAN) for facial feature analysis
- Dynamically changes advertisements according to user demographics and behavior
- Aims to improve ad engagement without directly collecting personal data

Highlights:
- Uses a unique matching mechanism to correlate facial features with business goals
- Can identify single or group users and provide customized content

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: - Captures initial image from a video stream
- Enhances image illumination and edges
- Detects vehicles based on homogenous properties of the vehicle body
- Filters noise based on geometric features and relationship to key objects
- Suitable for traffic monitoring in poor lighting conditions

Highlights:
- Unique method to filter noise based on geometric and relational properties
- Enhancement techniques that make this method suitable for poor lighting

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.

Source: Pirahansiah, F. “CV — Dr. Farshid Pirahansiah”. pirahansiah.com, updated 2026-09-27. Canonical URL https://pirahansiah.com/farshid/content/computer-vision/ · markdown source computer-vision.md · how to cite, quote and link this site.

Metric	Value
Citations	142
h-index	8
i10-index	5