---
title: 'UBIPr Database: Biometric Data & Publication Index'
url: https://www.emergentmind.com/topics/ubipr-database
type: topic
---

# UBIPr Database: Biometric Data & Publication Index

Searching arXiv for papers mentioning the UBIPr database and closely related terminology.
The term **UBIPr Database** is used in the supplied literature in two distinct ways. In biometric research, it denotes a visible-spectrum periocular image dataset acquired at multiple stand-off distances and used for periocular verification experiments, including cross-distance evaluation [2510.26282]. Separately, one source explicitly states that a search for the “UBIPr Database” corresponds to the **OLBIN publication database**, an optical long baseline interferometry bibliography hosted by the Jean-Marie Mariotti Center (JMMC) and integrated with the OLBIN web site [1007.5261]. The dominant technical usage in the provided data is the periocular dataset, for which the available description is substantially richer; however, the naming ambiguity is itself part of the record and affects citation, retrieval, and interpretation.

## 1. Nomenclature and scope

In the biometric usage documented here, the UBIPr database is a periocular dataset comprising **visible-spectrum periocular images captured with a CANON EOS 5D DSLR** under **cooperative** acquisition conditions [2510.26282]. The dataset is characterized by **multiple acquisition distances**, specifically **4 m, 5 m, 6 m, 7 m, and 8 m**, which induce controlled scale and resolution variation. The study summarized in the source uses **86 subjects**, **two sessions per subject**, and, for each distance, **one image per eye and per session**, yielding **344 images per distance** and **1,718 frontal periocular images** in total [2510.26282].

A separate usage appears in the interferometry literature. There, the source states that if one is searching for the “UBIPr Database,” it corresponds to the **OLBIN publication database**, described as an **up-to-date, curated bibliography for the optical long baseline interferometry community**, hosted and maintained by JMMC and linked to the OLBIN web site [1007.5261]. This resource is a publication database rather than an image dataset. The coexistence of these two usages suggests a bibliographic ambiguity rather than a shared technical object.

## 2. Dataset composition and acquisition conditions

The periocular UBIPr database, as used in the 2025 study, is defined by a controlled yet nontrivial acquisition setup. The imagery is **high-resolution**, with the paper noting **22.3 MP** for the CANON EOS 5D DSLR, and the subjects are cooperative [2510.26282]. The images are **frontal periocular images**, and the iris is explicitly **kept unmasked**, reflecting conditions in which **iris segmentation may be unreliable** [2510.26282].

The dataset includes five stand-off distances:

| Attribute | Value | Source |
|---|---:|---|
| Subjects | 86 | [2510.26282] |
| Sessions per subject | 2 | [2510.26282] |
| Distances | 4 m, 5 m, 6 m, 7 m, 8 m | [2510.26282] |
| Images per distance | 344 | [2510.26282] |
| Total images used | 1,718 | [2510.26282] |

For each distance, the image count is computed in the source as **86 × 2 eyes × 2 sessions = 344 images** [2510.26282]. This structure supports both intra-distance verification and inter-distance verification, allowing controlled analysis of performance degradation as acquisition distance changes.

The environmental variability reported in the source is limited but still relevant. **Glasses are present in some images**, and the paper notes that heatmap analysis indicates models tend to ignore eyeglass frames and focus on more consistent periocular skin regions [2510.26282]. Because the capture is cooperative and high quality, the source indicates reduced motion blur and reduced severe occlusions relative to more operational scenarios.

## 3. Region of interest definition and normalization

The periocular UBIPr protocol described in the source uses a geometry-driven normalization procedure. The authors perform **manual annotation of inner/outer sclera boundaries** to obtain a **robust sclera radius $R_s$** [2510.26282]. This is then used for **distance-specific normalization**, in which images in each distance group are resized to match the average $R_s$ of that group in order to equalize scale [2510.26282].

Cropping is performed using a **$7.6 R_s \times 7.6 R_s$ square** centered on the sclera center [2510.26282]. For orientation consistency, **left-eye crops are horizontally flipped** so that the **nose is on the left**, and **both eyes are treated as the same identity** [2510.26282]. The source emphasizes that the **full periocular region is retained** and that the iris is not masked.

These preprocessing choices are central to how the dataset is operationalized in later verification experiments. This suggests that reported performance depends not only on the raw image collection but also on a standardized normalization and cropping pipeline designed to reduce scale and orientation variability while preserving realistic periocular context.

## 4. Verification protocol and evaluation structure

The source provides a detailed verification protocol on UBIPr [2510.26282]. Two scenarios are defined: **intra-distance** and **inter-distance** verification.

For **intra-distance** evaluation, each distance $D_i$ is matched against itself. Genuine comparisons are computed as **session 1 vs session 2, same subject, both eyes cross-compared**, producing **4 comparisons per user** and therefore **344 genuine scores per distance**. Impostor comparisons are formed as **session 1 eyes vs session 2 eyes of all other users**, yielding **86 × 85 × 4 = 29,240 impostor scores per distance** [2510.26282].

For **inter-distance** evaluation, the source defines **10 ordered pairs** with $D_i$ vs $D_j$, $i \neq j$. Genuine scores are computed as **session 1 at $D_i$ vs both sessions at $D_j$**, with **8 comparisons per user**, giving **688 genuine scores per inter-distance pair**. The impostor count remains **29,240 impostor scores per distance pair** [2510.26282].

Across all **15 distance combinations**, the source reports totals of **8,600 genuine** and **438,600 impostor** scores [2510.26282]. Performance is summarized using **Equal Error Rate (EER)**.

The similarity functions reported are **cosine similarity** and **chi-square distance**. The source gives the definitions

$$
s_{\cos}(x, y) = \frac{x \cdot y}{\|x\|_2 \|y\|_2}
$$

and

$$
d_{\chi^2}(p, q) = \frac{1}{2} \sum_i \frac{(p_i - q_i)^2}{p_i + q_i}.
$$

The paper reports both measures at dataset level, while per-distance analysis focuses on **ImageNet initialization with $\chi^2$**, stated to be consistently best [2510.26282].

## 5. Use in CNN-based periocular verification

The most detailed arXiv treatment in the supplied material uses UBIPr to study **complementarity and explainability in CNNs for periocular verification across acquisition distances** [2510.26282]. Three architectures of increasing complexity are trained on a large periocular corpus derived from VGGFace2: **SqueezeNet**, **MobileNetv2**, and **ResNet50**.

The source reports the following architectural descriptions and training setup [2510.26282]:

- **SqueezeNet**: **18 layers**, approximately **1.24M parameters**, with **batch normalization added between conv and ReLU**.
- **MobileNetv2**: **53 layers**, approximately **3.5M parameters**, using **inverted residuals** and **depth-wise separable convolutions**.
- **ResNet50**: **50 layers**, approximately **25.6M parameters**, with **residual bottlenecks**.

All three are **implemented in MATLAB R2024b** and adapted to accept **113×113 inputs** by changing the **first convolution stride from 2 to 1** [2510.26282]. Input normalization is **subtract 127.5 and divide by 128**. Identity templates are taken from the **Global Average Pooling layer immediately before the classifier** [2510.26282].

Two initialization regimes are compared: **ImageNet initialization** and **“Face” initialization**, where the model is first trained on **MS-Celeb-1M (RetinaFace-cleaned MS1M; 5.1M images, 93.4K classes)** and then fine-tuned on **VGGFace2** [2510.26282]. Large-scale periocular pretraining uses **more than 1.9M ocular crops derived from VGGFace2**, specifically **1,907,572 ocular crops** obtained from **953,786 valid faces**, or approximately **221 crops/identity on average** [2510.26282].

The training objective is **ocular identification via cross-entropy loss** on VGGFace2 crops, using **SGDM**, **batch size 128**, and a staged learning-rate schedule **0.01 → 0.005 → 0.001 → 0.0001**, reduced when validation plateaus, with **2% validation per user** [2510.26282].

## 6. Reported performance, fusion, and explainability

On pooled intra- and inter-distance trials, the source reports that **ResNet50** is the best single model and that **$\chi^2$ consistently outperforms cosine** for each backbone [2510.26282]. The dataset-level EER values are:

| Model / setup | Cosine EER | $\chi^2$ EER |
|---|---:|---:|
| SqueezeNet, ImageNet | 5.44% | 4.93% |
| MobileNetv2, ImageNet | 2.12% | 2.10% |
| ResNet50, ImageNet | 1.73% | 1.66% |
| SqueezeNet, Face | 5.97% | 5.45% |
| MobileNetv2, Face | 2.24% | 2.15% |
| ResNet50, Face | 1.95% | 1.93% |

The source also states that **ImageNet initialization outperforms “face” initialization across all networks and metrics**, attributing this to more generic features that adapt better to periocular data [2510.26282].

Score-level fusion is performed with **linear logistic regression**,

$$
p(\mathrm{match}\mid s) = \sigma\!\left(w_0 + \sum_{j=1}^{m} w_j s_j\right),
$$

with $\sigma(z) = 1/(1 + e^{-z})$ [2510.26282]. Under **ImageNet + $\chi^2$**, the best pooled result is obtained by fusing **all three** networks, achieving **1.31% EER**, which the source describes as a **new state-of-the-art** on UBIPr under the paper’s protocol [2510.26282]. Relative to the best single network at **1.66%**, this corresponds to a **−21.14%** relative improvement in EER [2510.26282].

Per-distance behavior is also specified. For intra-distance verification, performance is **stable at 4–7 m**, while the **8 m** condition is the most challenging [2510.26282]. For inter-distance verification, accuracy degrades with larger distance gaps, most noticeably for **SqueezeNet**, whereas **ResNet50 remains robust with EERs under 2% even for 4 m gap** [2510.26282]. The all-network fusion **typically achieves EER < 1.5% and sometimes < 1% across most intra- and inter-distance scenarios** [2510.26282].

The same study uses **LIME heatmaps** and **Jensen–Shannon divergence (JSD)** to analyze complementarity [2510.26282]. The reported qualitative pattern is that **MobileNetv2** attends more locally, often **under the lower eyelid**, whereas **ResNet50** and **SqueezeNet** show broader patterns involving **upper eyelid, sclera, and tear duct**. The **pupil/iris receives less attention**, indicating reliance on **periocular context rather than iris texture** [2510.26282]. Pairwise JSD correlations are reported as **0.193**, **0.292**, and **0.559**, supporting the conclusion that the networks focus on distinct regions and therefore benefit from fusion [2510.26282].

## 7. Ambiguity, limitations, and related usage

A notable issue is terminological ambiguity. The supplied interferometry source explicitly states that the resource corresponding to the “UBIPr Database” is the **OLBIN publication database**, not a biometric image corpus [1007.5261]. That OLBIN resource is built on **MySQL**, integrated with **NASA ADS**, classified by approximately **70 tags grouped into seven categories**, and used to generate community statistics in optical long baseline interferometry [1007.5261]. It is therefore a fundamentally different type of database. The overlap in naming is a bibliographic complication rather than a conceptual relation.

Within the biometric usage, the source notes several limitations. The principal one is **potential domain shift**, since models trained on **VGGFace2 eye crops** are evaluated on UBIPr, and **generalization to lower-quality sensors and non-cooperative scenarios remains to be tested** [2510.26282]. The source also reports visible sensitivity at the **farthest range (8 m)** and for **larger inter-distance gaps**, especially in weaker architectures [2510.26282]. Glasses are present in some images; although models tend to ignore the frames, attention variability around **eyelashes** and **tear duct** can still lead to divergence [2510.26282].

The same paper outlines several future directions: evaluating on **surveillance-grade sensors** and **non-cooperative subjects**, exploring **near-infrared periocular data**, considering **spectrum translation** to build sufficient NIR training data, and integrating **margin-based losses** such as **ArcFace** together with **sequential fine-tuning (MS1M → VGGFace2)** [2510.26282].

Taken together, the supplied record supports two encyclopedia-level meanings for “UBIPr Database.” In current biometric usage, it denotes a periocular verification benchmark with controlled multi-distance acquisition, strong utility for cross-distance evaluation, and a demanding protocol that has recently been used to demonstrate **1.31% pooled EER** through CNN fusion [2510.26282]. In a separate bibliographic usage, the same label is mapped to the OLBIN/JMMC interferometry publication database, a curated scholarly index rather than an image dataset [1007.5261].

Source: https://www.emergentmind.com/topics/ubipr-database